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IEEE Transactions on Robotics
Journal Prestige (SJR): 1.822  Citation Impact (citeScore): 6 Number of Followers: 71  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1546-1904 - ISSN (Online) 1552-3098 Published by IEEE  [228 journals]
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- Deep Learning-Based Automatic Control of Magnetic Diatom Biohybrid
Microrobots for Targeted Delivery-
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Authors: Mengyue Li;Liang Li;Junjian Zhou;Lianqing Liu;Niandong Jiao;
Pages: 2990 - 3003
Abstract: Biohybrid microrobots with autonomous movement capabilities have broad application prospects in targeted delivery, attracting researchers to study their movement characteristics. However, its automatic control is still challenging, and exploring real-time detection of its environment for path planning to achieve stable closed-loop control is highly important for its practical application. Here, we applied deep learning for the detection of biohybrid microrobots and their targets and obstacles, followed by real-time path planning and trajectory tracking of biohybrid microrobots for targeted delivery. The proposed detection algorithm introduces attention and multiscale feature fusion mechanisms in YOLOv7 algorithm (AM-YOLOv7) with the aim of enhancing the precision of detecting small-scale targets when robots, obstacles and targets are displayed globally, and the detection capabilities are verified through simulations and experiments. The proposed planning algorithm introduces a turning penalty function and a path smoothing strategy into A* algorithm (PS-A*) to make the planned path short and smooth, which has been verified through simulation and experiments. The adaptive fuzzy PID method is used to track the robot's trajectory, and experiments and simulations show that the biohybrid microrobot can move according to the preset trajectory better. The final cell scene experimental results show that the biohybrid microrobot using this system can effectively avoid obstacle cells and be delivered to target cells. The system can detect biohybrid microrobots, obstacle cells and target cells, plan short and smooth trajectories, and track them accurately. The proposed method has certain generalizability and broad application prospects in targeted delivery.
PubDate: FRI, 18 APR 2025 09:16:31 -04
Issue No: Vol. 41, No. null (2025)
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- Load-Transfer Suspended Backpack With Bioinspired Vibration Isolation for
Shoulder Pressure Reduction Across Diverse Terrains-
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Authors: Yu Cao;Mengshi Zhang;Jian Huang;Samer Mohammed;
Pages: 3059 - 3077
Abstract: Active suspended backpacks represent a promising solution to mitigate the impact of inertial forces on individuals engaged in load carriage. However, identifying effective control objectives aimed at enhancing human carrying capacity remains a significant challenge. In this study, we introduce a novel approach by integrating a limb-like structure-type (LLS) bioinspired vibration isolator, modeled using Lagrangian mechanics, into an active load-transfer suspended backpack to primarily alleviate human shoulder pressure, thereby constructing a human–robot interaction control framework for the system. Drawing from a double-mass coupled oscillator model, this approach formulates a vertical dynamics model for the human-backpack system, systematically exploring the principles of both static load transfer and dynamic load reduction on the human shoulder. Subsequently, a series-elastic-actuator-based controller with prescribed performance is proposed to simultaneously achieve trajectory tracking and ensure load motion within the limited range. Theoretically, we validate the input–output stability of the LLS model and guarantee the ultimate uniform boundedness of the closed-loop system. Simulation and experimental trials conducted across different terrain scenarios validate the effectiveness of the proposed method, highlighting reductions of 18.68% in metabolic rate during level ground walking, 9.58% in a staircase scenario, and 12.35% in a complex terrain, involving uphill, downstairs, and flat ground walking.
PubDate: MON, 21 APR 2025 09:16:51 -04
Issue No: Vol. 41, No. null (2025)
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- Shear-Based Grasp Control for Multifingered Underactuated Tactile Robotic
Hands-
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Authors: Christopher J. Ford;Haoran Li;Manuel G. Catalano;Matteo Bianchi;Efi Psomopoulou;Nathan F. Lepora;
Pages: 3113 - 3128
Abstract: This article presents a shear-based control scheme for grasping and manipulating delicate objects with a Pisa/IIT anthropomorphic SoftHand equipped with soft biomimetic tactile sensors on all five fingertips. These “microTac” tactile sensors are miniature versions of the TacTip vision-based tactile sensor, and can extract precise contact geometry and force information at each fingertip for use as feedback into a controller to modulate the grasp while a held object is manipulated. Using a parallel processing pipeline, we asynchronously capture tactile images and predict contact pose and force from multiple tactile sensors. Consistent pose and force models across all sensors are developed using supervised deep learning with transfer learning techniques. We then develop a grasp control framework that uses contact force feedback from all fingertip sensors simultaneously, allowing the hand to safely handle delicate objects even under external disturbances. This control framework is applied to several grasp-manipulation experiments: First, retaining a flexible cup in a grasp without crushing it under changes in object weight; Second, a pouring task where the center of mass of the cup changes dynamically; and third, a tactile-driven leader-follower task where a human guides a held object. These manipulation tasks demonstrate more human-like dexterity with underactuated robotic hands by using fast reflexive control from tactile sensing.
PubDate: MON, 21 APR 2025 09:16:52 -04
Issue No: Vol. 41, No. null (2025)
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- Aerial Robots Carrying Flexible Cables: Dynamic Shape Optimal Control via
Spectral Method Model-
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Authors: Yaolei Shen;Antonio Franchi;Chiara Gabellieri;
Pages: 3162 - 3182
Abstract: In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations combined with boundary conditions described by ordinary differential equations. The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced-order model, nonlinear model predictive control is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method-based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned proportional–integral–derivative (PID) controller (allowing to close the control loop on the quadrotor state only).
PubDate: FRI, 18 APR 2025 09:16:31 -04
Issue No: Vol. 41, No. null (2025)
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- Model Predictive Inferential Control of Neural State-Space Models for
Autonomous Vehicle Motion Planning-
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Authors: Iman Askari;Ali Vaziri;Xuemin Tu;Shen Zeng;Huazhen Fang;
Pages: 3202 - 3222
Abstract: Model predictive control (MPC) has proven useful in enabling safe and optimal motion planning for autonomous vehicles. In this article, we investigate how to achieve MPC-based motion planning when a neural state-space model represents the vehicle dynamics. As the neural state-space model will lead to highly complex, nonlinear, and nonconvex optimization landscapes, mainstream gradient-based MPC methods will struggle to provide viable solutions due to heavy computational load. In a departure, we propose the idea of model predictive inferential control (MPIC), which seeks to infer the best control decisions from the control objectives and constraints. Following this idea, we convert the MPC problem for motion planning into a Bayesian state estimation problem. Then, we develop a new implicit particle filtering/smoothing approach to perform the estimation. This approach is implemented as banks of unscented Kalman filters/smoothers and offers high sampling efficiency, fast computation, and estimation accuracy. We evaluate the MPIC approach through a simulation study of autonomous driving in different scenarios, along with an exhaustive comparison with gradient-based MPC. The simulation results show that the MPIC approach has considerable computational efficiency despite complex neural network architectures and the capability to solve large-scale MPC problems for neural state-space models.
PubDate: THU, 01 MAY 2025 09:17:15 -04
Issue No: Vol. 41, No. null (2025)
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- Double Oracle Algorithm for Game-Theoretic Robot Allocation on Graphs
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Authors: Zijian An;Lifeng Zhou;
Pages: 3244 - 3259
Abstract: In this article, we study the problem of game-theoretic robot allocation where two players strategically allocate robots to compete for multiple sites of interest. Robots possess offensive or defensive capabilities to interfere and weaken their opponents to take over a competing site. This problem belongs to the conventional an acronym colonel blotto game (CBG). Considering the robots' heterogeneous capabilities and environmental factors, we generalize the conventional Blotto game by incorporating heterogeneous robot types and graph constraints that capture the robot transitions between sites. Then, we employ the double oracle algorithm (DOA) to solve for the Nash equilibrium of the generalized Blotto game. Particularly, for cyclic-dominance-heterogeneous (CDH) robots that inhibit each other, we define a new transformation rule between any two robot types. Building on the transformation, we design a novel utility function to measure the game's outcome quantitatively. Moreover, we rigorously prove the correctness of the designed utility function. Finally, we conduct extensive simulations to demonstrate the effectiveness of DOA on computing Nash equilibrium for homogeneous, linear heterogeneous, and CDH robot allocation on graphs.
PubDate: TUE, 06 MAY 2025 09:16:25 -04
Issue No: Vol. 41, No. null (2025)
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- FlowSight: Vision-Based Artificial Lateral Line Sensor for Water Flow
Perception-
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Authors: Tiandong Zhang;Rui Wang;Qiyuan Cao;Shaowei Cui;Gang Zheng;Shuo Wang;
Pages: 3260 - 3277
Abstract: This article presents a novel vision-based artificial lateral line (ALL) sensor, FlowSight, enhancing the perception capabilities of underwater robots. Through an autonomous vision system, FlowSight allows for simultaneous sensing the speed and direction of local water flow without relying on external auxiliary equipment. Inspired by the lateral line neuromast of fish, a flexible bionic tentacle is designed to sense water flow. Deformation and motion characteristics of the tentacle are modeled and analyzed using bidirectional fluid-structure interaction (FSI) simulation. Upon contact with water flow, the tentacle converts water flow information into elastic deformation information, which is captured and processed into an image sequence by the autonomous vision system. Subsequently, a water flow perception method based on deep neural networks is proposed to estimate the flow speed and direction from the captured image sequence. The perception network is trained and tested using data collected from practical experiments conducted in a controllable swim tunnel. Finally, the FlowSight sensor is integrated into the bionic underwater robot RoboDact, and a closed-loop motion control experiment based on water flow perception is conducted. Experiments conducted in the swim tunnel and water pool demonstrate the feasibility and effectiveness of FlowSight sensor and the water flow perception method.
PubDate: TUE, 06 MAY 2025 09:16:17 -04
Issue No: Vol. 41, No. null (2025)
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- Representation of Human arm Dynamic Intents With an Electrical Impedance
Tomography (EIT)-Driven Musculoskeletal Model for Human–Robot
Interaction-
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Authors: Enhao Zheng;Xiaodong Liu;Chenfeng Xu;Zhihao Zhou;Qining Wang;
Pages: 3278 - 3296
Abstract: Representing human arm dynamic intent is essential for effective human–robot interaction. Accurately and robustly decoding these intentions through mathematical modeling of neuromuscular processes poses significant challenges. This study introduces an electrical impedance tomography (EIT)-driven musculoskeletal model, which integrates an EIT sensing system with methods for muscle identification, parameter estimation, and musculoskeletal system modeling. Unlike existing muscle-signal techniques, EIT captures muscle activities from the anatomical cross-sectional plane, providing both activation dynamics and morphological features. We validated our method through multiDoF wrist kinematics estimation under varying contraction intensities, arm endpoint stiffness estimation, and robotic variable admittance control. Our approach achieves accuracy comparable to state-of-the-art methods while requiring fewer training samples and a more compact sensing system. The model incorporates physiological constraints, minimizing decoding errors, and ensuring interaction safety. This method enables reliable intent decoding with practical training demands. Future work will enhance the EIT system for complex tasks.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- A Model Predictive Capture Point Control Framework for Robust Humanoid
Balancing Via Ankle, Hip, and Stepping Strategies-
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Authors: Myeong-Ju Kim;Daegyu Lim;Gyeongjae Park;Kwanwoo Lee;Jaeheung Park;
Pages: 3297 - 3316
Abstract: The robust balancing capability of humanoids is essential for mobility in real environments. Many studies focus on implementing human-inspired ankle, hip, and stepping strategies to achieve human-level balance. In this article, a robust balance control framework for humanoids is proposed. First, a model predictive control (MPC) framework is proposed for capture point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. In addition, a variable weighting method is introduced that adjusts the weighting parameters of the centroidal angular momentum damping control. Second, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated compared to a state-of-the-art quadratic programming-based CP controller that employs the ankle, hip, and stepping strategies.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Development of Bioinspired Five-DOF Origami for Robotic Spine Assistive
Exoskeleton-
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Authors: Bing Chen;Xiang Ni;Lei Zhou;Bin Zi;Eric Li;Dan Zhang;
Pages: 3317 - 3334
Abstract: Frequent and high-load manual material handling (MMH) tasks often cause back injuries to the workers, and back-support exoskeletons are developed for individuals with MMH tasks. However, these exoskeletons usually cannot adapt well to the movements of the wearer's spine. This article introduces a new bioinspired five degree of freedom (DOF) origami, and via mechanical design, a unique rigid-flexible coupled bioinspired origami mechanism is proposed. This origami mechanism is compact and lightweight, and it has stable kinematic behaviors. With the designed origami mechanisms, a novel active origami-based robotic spine assistive exoskeleton (OSAE) is developed to assist individuals with MMH tasks during the symmetric and asymmetric lifting. The OSAE is actuated by a cable-driven module through an underactuated spine module that consists of seven origami mechanisms. With the designed spine module, the OSAE can adapt well to the wearer's spine motions during MMH tasks. Modeling of the five-DOF origami is described, and an adaptive control strategy is proposed for the exoskeleton to adapt to different lifting methods and objects with different weights. The experimental results demonstrate the effectiveness of the proposed OSAE. During the symmetric lifting of a 10-kg object, a reduction of 41.28% of the average muscle activity of the wearer's lumbar erector spinae muscle (LES) is observed, and reductions of 30.15% and 39.54% of the average muscle activities of the wearer's left and right LES are observed, respectively, during the asymmetric lifting of a 10-kg object.
PubDate: TUE, 06 MAY 2025 09:16:25 -04
Issue No: Vol. 41, No. null (2025)
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- Formulating the Unicycle on the Sphere Path Planning Problem as a Linear
Time-Varying System-
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Authors: Federico Thomas;Jaume Franch;
Pages: 3335 - 3347
Abstract: The kinematics, dynamics, and control of a unicycle moving without slipping on a plane has been extensively studied in the literature of nonholonomic mechanical systems. However, since planar motion can be seen as a limiting case of the motion on a sphere, we focus our analysis on the more general spherical case. This article introduces a novel approach to path planning for a unicycle rolling on a sphere while satisfying the nonslipping constraint. Our method is based on a simple yet effective idea: first, we model the system as a linear time-varying dynamic system. Then, leveraging the fact that certain such systems can be integrated under specific algebraic conditions, we derive a closed-form expression for the control variables. This formulation includes three free parameters, which can be tuned to generate a path connecting any two configurations of the unicycle. Notably, our approach requires no prior knowledge of nonholonomic system analysis, making it accessible to a broader audience.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Large-Scale Multirobot Coverage Path Planning on Grids With Path
Deconfliction-
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Authors: Jingtao Tang;Zining Mao;Hang Ma;
Pages: 3348 - 3367
Abstract: In this article, we study multirobot coverage path planning (MCPP) on a four-neighbor 2-D grid $G$, which aims to compute paths for multiple robots to cover all cells of $G$. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid $\mathcal {H}$ and then employ the spanning tree coverage (STC) paradigm to generate paths on $G$, making them inapplicable to grids with partially obstructed $2 \times 2$ blocks. To address this limitation, we reformulate the problem directly on $G$, revolutionizing grid-based MCPP solving and establishing new NP-hardness results. We introduce extended STC (ESTC), a novel paradigm that extends STC to ensure complete coverage with bounded suboptimality, even when $\mathcal {H}$ includes partially obstructed blocks. Furthermore, we present LS-MCPP, a new algorithmic framework that integrates ESTC with three novel types of neighborhood operators within a local search strategy to optimize coverage paths directly on $G$. Unlike prior grid-based MCPP work, our approach also incorporates a versatile postprocessing procedure that applies multiagent path finding (MAPF) techniques to MCPP for the first time, enabling a fusion of these two important fields in multirobot coordination. This procedure effectively resolves inter-robot conflicts and accommodates turning costs by solving an MAPF variant, making our MCPP solutions more practical for real-world applications. Extensive experiments demonstrate that our approach significantly improves solution quality and efficiency, managing up to 100 robots on grids as large as $\text{256} \times \text{256}$ within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.
PubDate: TUE, 06 MAY 2025 09:16:17 -04
Issue No: Vol. 41, No. null (2025)
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- AiDT: Toward Radar-Based Joint Anti-Interference Detection and Tracking
for Weak Extended Targets Under Zero-Trust Autonomous Perception Tasks-
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Authors: Zhenyuan Zhang;Yu Zhang;Darong Huang;Xin Fang;Mu Zhou;Ying Zhang;
Pages: 3368 - 3384
Abstract: Extended object detection and tracking (EODT) is becoming a promising alternative for autonomous perception, which provides not only common motion states but also accurate spatial extent information, such as shape and size estimations. However, due to uncoordinated radar transmissions in zero-trust autonomous driving scenarios, radar-based EODT systems suffer from mutual radio frequency (RF) interference launched by attackers, leading to ghost targets and increased noise. On this account, a novel joint anti-interference detection and tracking system for weak extended targets is presented in this article. In contrast to pioneering works that treat object detection and tracking as two separate steps, the proposed method handles them jointly by integrating a continuous detection process into tracking, improving the detectability of weak targets. More specifically, to accommodate the time-varying number and extended size of radar reflections, an adaptive spatial distribution model representing the deformable extents is incorporated to capture the contour evolution over time. The key insight is that by accumulating the reflected power, all backscattered points are regarded as one entity to match the real target so that the intractable data association problem can be circumvented in the proposed method. Unlike the prominent random matrix model-based approaches that split motion and extent states into independent parts, this study explores the interdependencies between the states and updates them simultaneously. In addition, the proposed system has been deployed on a low-cost automotive radar platform. Experimental results confirm that the proposed approach can achieve accurate and resilient EODT against RF interference attacks, especially in occlusion, dynamic motion switching, and complex multiple extended target tracking scenarios.
PubDate: FRI, 09 MAY 2025 09:16:35 -04
Issue No: Vol. 41, No. null (2025)
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- R-FAC: Resilient Value Function Factorization for Multirobot Efficient
Search With Individual Failure Probabilities-
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Authors: Hongliang Guo;Qi Kang;Wei-Yun Yau;Chee-Meng Chew;Daniela Rus;
Pages: 3385 - 3401
Abstract: This article investigates the resilient multirobot efficient search problem (R-MuRES), which aims at coordinating multiple robots to detect a “nonadversarial” moving target with the minimal expected time. One unique characteristic of R-MuRES among others is the possibility of individual robot's malfunction and withdrawal from the team during task execution, which results in a variable number of searchers in the deployment phase and entails that the possibility of team member failures must be considered during the planning stage, particularly in the training phase. We propose a resilient value function factorization (R-FAC) paradigm, which constructs the central value function from individual ones in a resilient manner, taking into account individual robots' failures, and ensures that the constructed central value function has the minimal mean squared temporal difference error across various team compositions. R-FAC stipulates that the individual global maximum principle is satisfied for whichever team configuration and thus any functioning robot contributes positively to the remaining team, as long as it executes the greedy policy with respect to the factorized individual value function. Subsequently, we introduce the variational value decomposition network (V2DN) as one of the instantiated R-FAC algorithms. V2DN employs the $\log$-sum-$\exp$ mechanism to construct the central value function from individual ones, enabling it to take a varying number of robots' individual value functions as inputs. Then, we explain why, specifically for the multirobot search task, the $\log$-sum-$\exp$ mechanism is superior to the brute-force summation operation used in the canonical value decomposition network (VDN), and compare V2DN with state-of-the-art MuRES solutions as well as the vanilla VDN algorithm in two canonical MuRES testing environments and show that it achieves the best resiliency score when one or several individual robots quit the team during task execution. Furthermore, we validate V2DN with a real multirobot system in a self-constructed indoor environment as the proof of concept.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Design and Control of a Musculoskeletal Bionic Leg With Optimized and
Sensorized Soft Artificial Muscles-
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Authors: Xuguang Dong;Yixin Wang;Jingyi Zhou;Xin An;Yinglei Zhu;Fugui Xie;Xin-Jun Liu;Huichan Zhao;
Pages: 3402 - 3422
Abstract: The development of high-performance bionic legged robots can benefit from the continued advancements in various actuation methods, such as artificial muscles. This work presents a musculoskeletal bionic leg driven by fluidic elastomer actuators (FEAs), showcasing their potential as artificial muscles for legged robots. Our approach integrates three key innovations: First, we established a mechanics model using thin plate theory to optimize the bellows shell structure of the FEAs, achieving high force output while maintaining inherent compliance. Second, we developed a lightweight embedded optoelectronic sensing system that enables closed-loop control without significantly increasing mass. Third, we designed a two-joint leg in the sagittal plane that utilizes a bionic configuration incorporating both monoarticular and biarticular FEAs. The leg demonstrated robust performance across various tasks including extreme positional movements, load-bearing squats supporting up to 2.45 times its body weight, vertical jumping with 147 mm ground clearance, and stable walking. Notably, our embedded sensing system successfully detected ground contact states without additional foot sensors, enabling reliable gait control while minimizing complexity and weight. The experimental results validate both the mechanical capabilities of the optimized FEAs and their controllability through embedded sensing, laying a foundation for developing full legged robots with muscle-like actuation.
PubDate: WED, 07 MAY 2025 09:16:02 -04
Issue No: Vol. 41, No. null (2025)
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- From Extended Environment Perception Toward Real-Time Dynamic Modeling for
Long-Range Underwater Robot-
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Authors: Lei Lei;Yu Zhou;Jianxing Zhang;
Pages: 3423 - 3441
Abstract: Underwater robots are critical observation platforms for diverse ocean environments. However, existing robotic designs often lack long-range and deep-sea observation capabilities and overlook the effects of environmental uncertainties on robotic operations. This article presents a novel long-range underwater robot for extreme ocean environments, featuring a low-power dual-circuit buoyancy adjustment system, an efficient mass-based attitude adjustment system, flying wings, and an open sensor cabin. After that, an extended environment perception strategy with incremental updating is proposed to understand and predict full hydrological dynamics based on sparse observations. On this basis, a real-time dynamic modeling approach integrates multibody dynamics, perceived hydrological dynamics, and environment-robot interactions to provide accurate dynamics predictions and enhance motion efficiency. Extensive simulations and field experiments covering 600 km validated the reliability and autonomy of the robot in long-range ocean observations, highlighting the accuracy of the extended perception and real-time dynamics modeling methods.
PubDate: TUE, 06 MAY 2025 09:16:25 -04
Issue No: Vol. 41, No. null (2025)
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- Safe Reinforcement Learning on the Constraint Manifold: Theory and
Applications-
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Authors: Puze Liu;Haitham Bou-Ammar;Jan Peters;Davide Tateo;
Pages: 3442 - 3461
Abstract: Integrating learning-based techniques, especially reinforcement learning, into robotics is promising for solving complex problems in unstructured environments. Most of the existing approaches rely on training in carefully calibrated simulators before being deployed on real robots, often without real-world fine-tuning. While effective in controlled settings, this framework falls short in applications where precise simulation is unavailable or the environment is too complex to model. Instead, on-robot learning, which learns by interacting directly with the real world, offers a promising alternative. One major problem for on-robot reinforcement learning is ensuring safety, as uncontrolled exploration can cause catastrophic damage to the robot or the environment. Indeed, safety specifications, often represented as constraints, can be complex and nonlinear, making safety challenging to guarantee in learning systems. In this article, we show how we can impose complex safety constraints on learning-based robotics systems in a principled manner, both from theoretical and practical points of view. Our approach is based on the concept of the constraint manifold, representing the set of safe robot configurations. Exploiting differential geometry techniques, i.e., the tangent space, we can construct a safe action space, allowing learning agents to sample arbitrary actions while ensuring safety. We demonstrate the method's effectiveness in a real-world robot air hockey task, showing that our method can handle high-dimensional tasks with complex constraints.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Leveraging Geometric Modeling-Based Computer Vision for Context Aware
Control in a Hip Exosuit-
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Authors: Enrica Tricomi;Giuseppe Piccolo;Federica Russo;Xiaohui Zhang;Francesco Missiroli;Sandro Ferrari;Letizia Gionfrida;Fanny Ficuciello;Michele Xiloyannis;Lorenzo Masia;
Pages: 3462 - 3479
Abstract: Human beings adapt their motor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptive motor behavior has increased interest in integrating computer vision (CV) algorithms into robotic assistive technologies, marking a shift toward context aware control. However, such integration has rarely been achieved so far, with current methods mostly relying on data-driven approaches. In this study, we introduce a novel control framework for a soft hip exosuit, employing instead a physics-informed CV method grounded on geometric modeling of the captured scene for assistance tuning during stairs and level walking. This approach promises to provide a viable solution that is more computationally efficient and does not depend on training examples. Evaluating the controller with six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of $93.0\pm 1.1\%$. CV-based assistance provided significantly greater metabolic benefits compared to non-vision-based assistance, with larger energy reductions relative to being unassisted during stair ascent ($-18.9 \pm 4.1\%$ versus $-5.2 \pm 4.1\%$) and descent ($-10.1 \pm 3.6\%$ versus $-4.7 \pm 4.8\%$). Such a result is a consequence of the adaptive nature of the device, enabled by the context aware controller that allowed for more effective walking support, i.e., the assistive torque showed a significant increase while ascending stairs ($+33.9\pm 8.8\%$) and decrease while descending stairs ($-17.4\pm 6.0\%$) compared to a condition without assistance modulation enabled by vision. These results highlight the potential of the approach, promoting effective real-time embedded applications in assistive robotics.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Communication- and Computation-Efficient Distributed Submodular
Optimization in Robot Mesh Networks-
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Authors: Zirui Xu;Sandilya Sai Garimella;Vasileios Tzoumas;
Pages: 3480 - 3499
Abstract: In this article, we provide a communication- and computation-efficient method for distributed submodular optimization in robot mesh networks. Submodularity is a property of diminishing returns that arises in active information gathering such as mapping, surveillance, and target tracking. Our method, resource-aware distributed greedy (RAG), introduces a new distributed optimization paradigm that enables scalable and near-optimal action coordination. To this end, RAG requires each robot to make decisions based only on information received from and about their neighbors. In contrast, the current paradigms allow the relay of information about all robots across the network. As a result, RAG’s decision-time scales linearly with the network size, while state-of-the-art near-optimal submodular optimization algorithms scale cubically. We also characterize how the designed mesh-network topology affects RAG’s approximation performance. Our analysis implies that sparser networks favor scalability without proportionally compromising approximation performance: while RAG’s decision-time scales linearly with network size, the gain in approximation performance scales sublinearly. We demonstrate RAG’s performance in simulated scenarios of area detection with up to 45 robots, simulating realistic robot-to-robot (r2r) communication speeds such as the 0.25 Mb/s speed of the Digi XBee 3 Zigbee 3.0. In the simulations, RAG enables real-time planning, up to three orders of magnitude faster than competitive near-optimal algorithms, while also achieving superior mean coverage performance. To enable the simulations, we extend the high-fidelity and photo-realistic simulator AirSim by integrating a scalable collaborative autonomy pipeline to tens of robots and simulating r2r communication delays.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- CornerVINS: Accurate Localization and Layout Mapping for Structural
Environments Leveraging Hierarchical Geometric Representations-
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Authors: Yidi Zhang;Fulin Tang;Yihong Wu;
Pages: 3500 - 3517
Abstract: A compact and consistent map of surroundings is critical for intelligent robots to understand their situations and realize robust navigation. Most existing techniques rely on infinite planes, which are sensitive to pose drift and may lead to confusing maps. Toward high-level perception in indoor environments, we propose CornerVINS, an innovative RGB-D inertial localization and layout mapping method leveraging hierarchical geometric features, i.e., points, planes, and box corners. Specifically, points are enhanced by fusing depth information, and planes are modeled as bounded patches using convex hulls to increase their discriminability. More importantly, box corners, lying at the intersection of three orthogonal planes, are parameterized with a 6-D vector and integrated into the extended Kalman filter for the first time. We introduce a hierarchical mechanism to effectively extract and associate planes and corners, which are considered as layout components of scenes and serve as long-term landmarks to correct camera poses. Extensive experiments prove that the proposed box corners bring significant improvements, enabling accurate localization and consistent layout mapping at low computational cost. Overall, the proposed CornerVINS outperforms state-of-the-art systems in both accuracy and efficiency.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Versatile Tasks on Integrated Aerial Platforms Using Only Onboard Sensors:
Control, Estimation, and Validation-
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Authors: Kaidi Wang;Ganghua Lai;Yushu Yu;Jianrui Du;Jiali Sun;Bin Xu;Antonio Franchi;Fuchun Sun;
Pages: 3518 - 3538
Abstract: Connecting multiple aerial vehicles to a rigid central platform through passive spherical joints holds the potential to construct a fully actuated aerial platform. The integration of multiple vehicles enhances efficiency in tasks like mapping and object reconnaissance. This article proposes a control and state estimation framework for the integrated aerial platform (IAP), enabling it to perform versatile tasks like object reconnaissance and physical interactive tasks with only onboard sensors. In the framework, the 6-D motion control serves as the low-level controller, while the high-level controller comprises a 6-D admittance filter and a perception-aware attitude correction module. The 6-D admittance filter, serving as the interaction controller, is adaptable for aerial interaction tasks. The perception-aware attitude correction algorithm is carefully designed by adopting a geometric model predictive controller (MPC). This algorithm, incorporating both offline and online calculations, proves to be well-suited for the intricate dynamics of an IAP. A 6-D direct wrench controller is also developed for the IAP. Notably, both the interaction controller and the direct wrench controller operate without reliance on force/torque sensors. Instead, a wrench observer algorithm is devised, considering external disturbances. In addition, based on the kinematics constraints of the multiple aerials in the platform, a fusion algorithm for multiple visual-inertial odometry and kinematics constraints is developed, providing more accurate localization. A prototype of the IAP is constructed, and its capabilities are demonstrated through experiments including perception-aware object reconnaissance, aerial mapping, aerial peg-in-hole task, and 6-D contact wrench generation. All experiments are conducted exclusively with onboard sensors. These tasks exemplify the merits of the proposed IAP and validate the effectiveness of the proposed control framework and fusion algorithm.
PubDate: FRI, 09 MAY 2025 09:16:35 -04
Issue No: Vol. 41, No. null (2025)
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- Embedded Hierarchical MPC for Autonomous Navigation
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Authors: Dennis Benders;Johannes Köhler;Thijs Niesten;Robert Babuška;Javier Alonso-Mora;Laura Ferranti;
Pages: 3556 - 3574
Abstract: To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Open-Loop Control of Electrically Conductive Materials in an Oscillating
Magnetic Field-
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Authors: Seth Stewart;Joseph Pawelski;Steve Ward;Andrew J. Petruska;
Pages: 3575 - 3589
Abstract: Control of objects using remotely generated magnetic fields has established itself as a viable option for 3-D position control, though the objects being manipulated to date have largely been limited to soft and hard-magnetic objects that react to a static magnetic field. This limits the application to a small subset of materials. This work presents the first analytically derived model for 3-D position control of any electrically conductive material subject to a time-varying magnetic field. By leveraging the induced eddy current and subsequent induced dipole, this model shows that conductive materials behave equivalently to diamagnetic materials and are, therefore, not subject to the limitations of the Earnshaw’s theorem, making stable, open-loop levitation possible. This is demonstrated by open-loop position control of a semibuoyant aluminum sphere.
PubDate: FRI, 18 APR 2025 09:16:31 -04
Issue No: Vol. 41, No. null (2025)
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- Meta-Learning Enhanced Model Predictive Contouring Control for Agile and
Precise Quadrotor Flight-
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Authors: Mingxin Wei;Lanxiang Zheng;Ying Wu;Ruidong Mei;Hui Cheng;
Pages: 3590 - 3608
Abstract: In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the quadrotor, especially during high-speed maneuvers. Traditional control models based on first principles struggle to capture these dynamics due to the complexity and variability of aerodynamic effects, which are challenging to model accurately. To address these challenges, this study proposes a meta-learning-based control strategy for accurately modeling quadrotor dynamics under varying speeds, treating each velocity condition as an independent learning task with a specifically trained neural network to ensure precise dynamic predictions. The meta-learning framework rapidly generates task-specific parameters adapted to speed variations by solving an optimization problem and employs an online incremental learning strategy to integrate real-time data for continuous model updates, enhancing system robustness. Regularization is introduced to prevent overfitting and improve generalizability. The integration of the meta-learned model into Model Predictive Contouring Control (MPCC) allows the system to achieve optimal control across different velocity levels, ensuring efficient and accurate flight control even during sharp turns and high-speed maneuvers. Extensive simulations and real-world experiments confirm that the proposed algorithm maintains a high level of control precision despite the nonlinear effects of rapid speed changes, complex flight trajectories and wind disturbances. The results highlight the advantages of combining meta-learning with adaptive control strategies, providing a robust framework for quadrotors operating in diverse and dynamic environments.
PubDate: TUE, 06 MAY 2025 09:16:25 -04
Issue No: Vol. 41, No. null (2025)
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- Autonomous Tomato Harvesting With Top–Down Fusion Network for
Limited Data-
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Authors: Xingxu Li;Yiheng Han;Nan Ma;Yongjin Liu;Jia Pan;Shun Yang;Siyi Zheng;
Pages: 3609 - 3628
Abstract: Using robots for tomato truss harvesting represents a promising approach to agricultural production. However, incomplete acquisition of perception information and clumsy operations often results in low harvest success rates or crop damage. To addressthis issue, we designed a new method for tomato truss perception, an autonomous harvesting method, and a novel circular rotary cutting end-effector. The robot performs object detection and keypoint detection on tomato trusses using the proposed top–down fusion network, making decisions on suitable targets for harvesting based on phenotyping and pose estimation. The designed end-effector moves gradually from the bottom up to wrap around the tomato truss, cutting the peduncle to complete the harvest. Experiments conducted in real-world scenarios for robotic perception and autonomous harvesting of tomato trusses show that the proposed method increases accuracy by up to 11.42% and 22.29% for complete and limited dataset conditions, compared to baseline models. Furthermore, we have implemented an automatic tomato harvesting system based on TDFNet, which reaches an average harvest success rate of 89.58% in the greenhouse.
PubDate: TUE, 06 MAY 2025 09:16:32 -04
Issue No: Vol. 41, No. null (2025)
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- Primitive-Swarm: An Ultra-Lightweight and Scalable Planner for Large-Scale
Aerial Swarms-
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Authors: Jialiang Hou;Xin Zhou;Neng Pan;Ang Li;Yuxiang Guan;Chao Xu;Zhongxue Gan;Fei Gao;
Pages: 3629 - 3648
Abstract: Achieving large-scale aerial swarms is challenging due to the inherent contradictions in balancing computational efficiency and scalability. This article introduces primitive-swarm, an ultra-lightweight and scalable planner designed specifically for large-scale autonomous aerial swarms. The proposed approach adopts a decentralized and asynchronous replanning strategy. Within it is a novel motion primitive library consisting of time-optimal and dynamically feasible trajectories. They are generated utilizing a novel time-optimal path parameterization algorithm based on reachability analysis. Then, a rapid collision checking mechanism is developed by associating the motion primitives with the discrete surrounding space according to conflicts. By considering both spatial and temporal conflicts, the mechanism handles robot-obstacle and robot–robot collisions simultaneously. Then, during a replanning process, each robot selects the safe and minimum cost trajectory from the library based on user-defined requirements. Both the time-optimal motion primitive library and the occupancy information are computed offline, turning a time-consuming optimization problem into a linear-complexity selection problem. This enables the planner to comprehensively explore the nonconvex, discontinuous 3-D safe space filled with numerous obstacles and robots, effectively identifying the best hidden path. Benchmark comparisons demonstrate that our method achieves the shortest flight time and traveled distance with a computation time of less than 1 ms in dense environments. Super large-scale swarm simulations, involving up to 1000 robots, running in real time, verify the scalability of our method. Real-world experiments validate the feasibility and robustness of our approach. The code will be released to foster community collaboration.
PubDate: MON, 26 MAY 2025 09:16:41 -04
Issue No: Vol. 41, No. null (2025)
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- Stable Object Placement Planning From Contact Point Robustness
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Authors: Philippe Nadeau;Jonathan Kelly;
Pages: 3669 - 3683
Abstract: We introduce a planner designed to guide robot manipulators in stably placing objects within complex scenes. Our proposed method reverses the traditional approach to object placement: our planner selects contact points first and then determines a placement pose that solicits the selected points. This is instead of sampling poses, identifying contact points, and evaluating pose quality. Our algorithm facilitates stability-aware object placement planning, imposing no restrictions on object shape, convexity, or mass density homogeneity, while avoiding combinatorial computational complexity. Our proposed stability heuristic enables our planner to find a solution about 20 times faster when compared to the same algorithm not making use of the heuristic and eight times faster than a state-of-the-art method that uses the traditional sample-and-evaluate approach. The proposed planner is also more successful in finding stable placements than the five other benchmarked algorithms. Derived from first principles and validated in ten real robot experiments, our approach provides a general and scalable solution to the problem of rigid object placement planning.
PubDate: FRI, 06 JUN 2025 09:16:29 -04
Issue No: Vol. 41, No. null (2025)
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- RoTipBot: Robotic Handling of Thin and Flexible Objects Using Rotatable
Tactile Sensors-
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Authors: Jiaqi Jiang;Xuyang Zhang;Daniel Fernandes Gomes;Thanh-Toan Do;Shan Luo;
Pages: 3684 - 3702
Abstract: This article introduces RoTipBot, a novel robotic system for handling thin, flexible objects. Different from previous works that are limited to singulating them using suction cups or soft grippers, RoTipBot can count multiple layers and then grasp them simultaneously in a single grasp closure. Specifically, we first develop a vision-based tactile sensor named RoTip that can rotate and sense contact information around its tip. Equipped with two RoTip sensors, RoTipBot rolls and feeds multiple layers of thin, flexible objects into the centre between its fingers, enabling effective grasping. Moreover, we design a tactile-based grasping strategy that uses RoTip’s sensing ability to ensure both fingers maintain secure contact with the object while accurately counting the number of fed objects. Extensive experiments demonstrate the efficacy of the RoTip sensor and the RoTipBot approach. The results show that RoTipBot not only achieves a higher success rate but also grasps and counts multiple layers simultaneously—capabilities not possible with previous methods. Furthermore, RoTipBot operates up to three times faster than state-of-the-art methods. The success of RoTipBot paves the way for future research in object manipulation using mobilized tactile sensors.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Dynamic Hysteresis Compensation for Tendon-Sheath Mechanism in Flexible
Surgical Robots Without Distal Perception-
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Authors: Qian Gao;Guanglin Ji;Minyi Sun;Yin Xiao;Huaiyuan Rao;Zhenglong Sun;
Pages: 3703 - 3721
Abstract: The accurate position transmission of tendon-sheath mechanisms (TSMs) is challenging but of significance to the flexible robot for minimally invasive surgery. The challenges are mainly attributed to the following: first, the tendon-elongation and its caused hysteresis that depend on the route configuration of the TSM and could result in misaligned position transmission; second, realistic surgical scenarios requiring the TSM with arbitrary and even time-varying route configurations; and third the absence of distal sensory feedback due to strict spatial constraints. Existing works are always devoted to tackling the first challenge yet evade the second and third. Here, a route-related tendon-elongation model is formulated to resolve the first challenge, and in response to the second, a route-sensing optical fiber is used. Obeying the third challenge, a feedforward hysteresis compensator is then developed to align the distal position of the tendon with the desired position. Our final contribution gives an application-oriented remedy for the foregoing methodologies. Applying our compensator on the challenging position transmission tasks subject to second and third challenges, the positional accuracy can be still maintained at around 97.50%; guided by the provided remedy, the surgical end-effector achieves submillimeter tip position accuracy. Extensive tests demonstrate that the pending concerns yet of great practical importance in existing related works are well resolved.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Tactile Elastography
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Authors: Yichen Xiang;Lifeng Zhu;Aiguo Song;Yongjie Jessica Zhang;
Pages: 3722 - 3737
Abstract: Elasticity is one of the representative parameters that reflect the mechanical properties of soft materials. Detecting the underneath elasticity distribution called elastography is a key step for understanding and interacting with objects. Existing solutions for capturing the interior elasticity distribution typically rely on expensive apparatus. In this work, the dense tactile signal captured by the high-resolution vision-based tactile sensor is introduced as a new modality for reconstructing 3-D elasticity distribution. We propose a model-based method, which exploits the tactile maps from active pressing trials for the elastography task. The interior elasticity distribution for nonrigid objects is reconstructed from an inverse physics model. We analyze the credibility of the estimated elasticity distribution obtained from our method. Varying design factors are also discussed. We experiment our method on a set of synthesized 3-D models and physical models in robot-assisted scenes. Various experimental results have been gathered, demonstrating the efficacy of our approach in perceiving elasticity distribution.
PubDate: FRI, 06 JUN 2025 09:16:29 -04
Issue No: Vol. 41, No. null (2025)
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- Learning Wrist Policies for Anthropomorphic Soft Power Grasping in Handle
and Door Manipulation-
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Authors: Florian Voigt;Abdeldjallil Naceri;Sami Haddadin;
Pages: 3738 - 3759
Abstract: In this work, we advance robotic grasping by incorporating wrist compliance in a unified hand–arm system inspired by human limb coordination. This integration improves grasping reliability and robustness through impedance and force learning in robotic arms. The compliant wrist system effectively compensates for uncertainties in object position and orientation. Employing a combined impedance-force control approach, we address diverse grasping and manipulation tasks in simulation. Successfully transferring the learned policy to a service humanoid mobile robot enables the seamless execution of grasping and opening tasks for various doors and handles without additional learning, using both fully actuated and underactuated robotic hands. Remarkably, our robust strategies yielded only one failure in 30 trials for the underactuated hand, even with up to 8 cm translation normal to the handle and $33^\circ$ rotation errors, and no failures for the fully actuated one with up to 12 cm translation and $30^\circ$ rotation. This significantly outperforms state-of-the-art end-to-end reinforcement learning approaches. Furthermore, we successfully tested and validated our approach across various constrained everyday tasks in different environments. Our proposed framework represents an advancement in the learning and execution of power grasping with compliant manipulation, achieving practically relevant performance.
PubDate: FRI, 06 JUN 2025 09:16:12 -04
Issue No: Vol. 41, No. null (2025)
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- BotVIO: A Lightweight Transformer-Based Visual–Inertial Odometry for
Robotics-
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Authors: Wenhui Wei;Yangfan Zhou;Yimin Hu;Zhi Li;Sen Wang;Xin Liu;Jiadong Li;
Pages: 3760 - 3778
Abstract: Visual–inertial odometry (VIO) provides a robust localization solution for simultaneous localization and mapping systems. Self-supervised VIO, a leading approach, has the advantage of not requiring extensive ground-truth labels. Regrettably, this method still poses challenges for robotic applications, particularly uncrewed aerial vehicles, due to its computational complexity arising from inadequate model designs. To address this bottleneck, we introduce BotVIO (where “Bot” refers to “robotics”), a transformer-based self-supervised VIO model, offering an excellent solution to alleviate computational burdens for robotics. Our lightweight backbone combines shallow CNNs with spatial–temporal-enhanced transformers to replace conventional architectures, while the minimalist cross-fusion module uses single-layer cross-attention to enhance multimodal interaction. Extensive experiments show that, during pose estimation, BotVIO achieves a remarkable 70.37% reduction in trainable parameters and a 74.85% decrease in inference speed, reaching up to 57.80 fps on an NVIDIA Jetson NX (10W&2CORE), while improving pose accuracy and robustness. For the benefit of the community, we make public the source code.1
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Constraint-Guided Online Data Selection for Scalable Data-Driven Safety
Filters in Uncertain Robotic Systems-
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Authors: Jason J. Choi;Fernando Castañeda;Wonsuhk Jung;Bike Zhang;Claire J. Tomlin;Koushil Sreenath;
Pages: 3779 - 3798
Abstract: As the use of autonomous robots expands in tasks that are complex and challenging to model, the demand for robust data-driven control methods that can certify safety and stability in uncertain conditions is increasing. However, the practical implementation of these methods often faces scalability issues due to the growing amount of data points with system complexity and a significant reliance on high-quality training data. In response to these challenges, this study presents a scalable data-driven controller that efficiently identifies and infers from the most informative data points for implementing data-driven safety filters. Our approach is grounded in the integration of a model-based certificate function-based method and Gaussian Process regression, reinforced by a novel online data selection algorithm that reduces time complexity from quadratic to linear relative to dataset size. Empirical evidence, gathered from successful real-world cart–pole swing-up experiments and simulated locomotion of a five-link bipedal robot, demonstrates the efficacy of our approach. Our findings reveal that our efficient online data selection algorithm, which strategically selects key data points, enhances the practicality and efficiency of data-driven certifying filters in complex robotic systems, significantly mitigating scalability concerns inherent in nonparametric learning-based control methods.
PubDate: FRI, 06 JUN 2025 09:16:29 -04
Issue No: Vol. 41, No. null (2025)
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- ERPoT: Effective and Reliable Pose Tracking for Mobile Robots Using
Lightweight Polygon Maps-
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Authors: Haiming Gao;Qibo Qiu;Hongyan Liu;Dingkun Liang;Chaoqun Wang;Xuebo Zhang;
Pages: 3799 - 3819
Abstract: This article presents an effective and reliable pose tracking solution, termed ERPoT, for mobile robots operating in large-scale outdoor and challenging indoor environments, underpinned by an innovative prior polygon map. Especially, to overcome the challenge that arises as the map size grows with the expansion of the environment, the novel form of a prior map composed of multiple polygons is proposed. Benefiting from the use of polygons to concisely and accurately depict environmental occupancy, the prior polygon map achieves long-term reliable pose tracking while ensuring a compact form. More importantly, pose tracking is carried out under pure LiDAR mode, and the dense 3-D point cloud is transformed into a sparse 2-D scan through ground removal and obstacle selection. On this basis, a novel cost function for pose estimation through point-polygon matching is introduced, encompassing two distinct constraint forms: point-to-vertex and point-to-edge. In this study, our primary focus lies on two crucial aspects: lightweight and compact prior map construction, as well as effective and reliable robot pose tracking. Both aspects serve as the foundational pillars for future navigation across diverse mobile platforms equipped with different LiDAR sensors in varied environments. Comparative experiments based on the publicly available datasets and our self-recorded datasets are conducted, and evaluation results show the superior performance of ERPoT on reliability, prior map size, pose estimation error, and runtime over the other six approaches. The corresponding code can be accessed online.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Hybrid Long Short-Term Motor Optimization and Control of a Walking
Exoskeleton-
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Authors: Pengbo Huang;Zhijun Li;Mengchu Zhou;Guoxin Li;Yang Song;Rongxin Cui;
Pages: 3820 - 3840
Abstract: This article presents a hybrid long short-term motor (HLSM) optimization and control approach for a walking exoskeleton. It consists of long-term global optimization, short-term local optimization, human-in-the-loop trajectory adaptation, and hybrid cerebellar model articulation controller (HCMAC). In the long-term global optimization, a graphic spiking neural network (SNN) is utilized for an optimal global path. Along the path, the short-term motor optimization includes footstep optimization and obtains a sequence of footsteps. While in response to the unexpected obstacles along the footstep sequence, a human-in-the-loop planning strategy is designed by a virtual impedance model between the centers of mass (COMs) of the human and the exoskeleton, regulating the COM of the exoskeleton and generating footstep adaptation of the exoskeleton such that the exoskeleton can avoid obstacles and maintain its original global trajectory. Moreover, considering the unmodeled dynamics, we propose an HCMAC based on an integral Lyapunov function, which is exploited to counteract the system’s nonlinear uncertainties, external disturbances, and reduces a relatively high computational cost. We validate the effectiveness of the HLSM planner and controller in a practical indoor setting. The results demonstrate the effectiveness of HLSM planning and control in a real scenario for a walking exoskeleton.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Active Inference for Bandit-Based Autonomous Robotic Exploration With
Dynamic Preferences-
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Authors: Shohei Wakayama;Alberto Candela;Paul Hayne;Nisar Ahmed;
Pages: 3841 - 3851
Abstract: Autonomous selection of optimal options for data collection from multiple alternatives is challenging in uncertain environments. When secondary information about options is accessible, such problems can be framed as contextual multiarmed bandits (CMABs). Neuroinspired active inference (AIF) has gained interest for its ability to balance exploration and exploitation using the expected free energy objective function. Unlike previous studies that showed the effectiveness of AIF-based strategy for CMABs using synthetic data, this study aims to apply AIF to realistic scenarios, using a simulated mineralogical survey site selection problem. Hyperspectral data from the next generation airborne visible–infrared imaging spectrometer at Cuprite, Nevada, serves as contextual information for predicting outcome probabilities, while geologists’ mineral labels represent outcomes. Monte Carlo simulations assess the robustness of AIF against changing expert preferences. Results show AIF requires fewer iterations than standard bandit approaches with real-world noisy and biased data, and performs better when outcome preferences vary online by adapting the selection strategy to align with expert shifts.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Variations of Augmented Lagrangian for Robotic Multicontact Simulation
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Authors: Jeongmin Lee;Minji Lee;Sunkyung Park;Jinhee Yun;Dongjun Lee;
Pages: 3852 - 3869
Abstract: The multicontact nonlinear complementarity problem (NCP) is a naturally arising challenge in robotic simulations. Achieving high performance in terms of both accuracy and efficiency remains a significant challenge, particularly in scenarios involving intensive contacts and stiff interactions. In this article, we introduce a new class of multicontact NCP solvers based on the theory of the augmented Lagrangian (AL). We detail how the standard derivation of AL in convex optimization can be adapted to handle multicontact NCP through the iteration of surrogate problem solutions and the subsequent update of primal-dual variables. Specifically, we present two tailored variations of AL for robotic simulations: the cascaded Newton-based augmented Lagrangian (CANAL) and the subsystem-based alternating direction method of multipliers (SubADMM). We demonstrate how CANAL can manage multicontact NCP in an accurate and robust manner, while SubADMM offers superior computational speed, scalability, and parallelizability for high degrees-of-freedom multibody systems with numerous contacts. Our results showcase the effectiveness of the proposed solver framework, illustrating its advantages in various robotic manipulation scenarios.
PubDate: FRI, 06 JUN 2025 09:16:29 -04
Issue No: Vol. 41, No. null (2025)
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- SG-Reg: Generalizable and Efficient Scene Graph Registration
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Authors: Chuhao Liu;Zhijian Qiao;Jieqi Shi;Ke Wang;Peize Liu;Shaojie Shen;
Pages: 3870 - 3889
Abstract: This article addresses the challenges of registering two rigid semantic scene graphs, an essential capability when an autonomous agent needs to register its map against a remote agent, or against a prior map. The handcrafted descriptors in classical semantic-aided registration, or the ground-truth annotation reliance in learning-based scene graph registration, impede their application in practical real-world environments. To address the challenges, we design a scene graph network to encode multiple modalities of semantic nodes: open-set semantic feature, local topology with spatial awareness, and shape feature. These modalities are fused to create compact semantic node features. The matching layers then search for correspondences in a coarse-to-fine manner. In the back end, we employ a robust pose estimator to decide transformation according to the correspondences. We manage to maintain a sparse and hierarchical scene representation. Our approach demands fewer GPU resources and fewer communication bandwidth in multiagent tasks. Moreover, we design a new data generation approach using vision foundation models and a semantic mapping module to reconstruct semantic scene graphs. It differs significantly from previous works, which rely on ground-truth semantic annotations to generate data. We validate our method in a two-agent simultaneous localization and mapping benchmark. It significantly outperforms the handcrafted baseline in terms of registration success rate. Compared to visual loop closure networks, our method achieves a slightly higher registration recall while requiring only 52 kB of communication bandwidth for each query frame.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Enhancing Grasping Diversity With a Pinch-Suction and Soft-Rigid Hybrid
Multimodal Gripper-
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Authors: Yuwen Zhao;Jiaqi Zhu;Jie Zhang;Siyuan Zhang;Maosen Shao;Zhiping Chai;Yimu Liu;Jianing Wu;Zhigang Wu;Jinxiu Zhang;
Pages: 3890 - 3907
Abstract: Multimodal grasping has emerged as a promising strategy to enhance the grasping diversity of grippers in response to the rapid expansion of application scenarios. Among various designs, the pinch-suction hybrid mechanism and the soft-rigid hybrid structure have proved to be two practical strategies to achieve multimodality. However, existing research on these two strategies still lacks simple and effective collaborative mechanisms to fully leverage the advantages of each mode while ensuring mutual noninterference. In this article, we propose a pinch-suction and soft-rigid hybrid multimodal gripper (HMG), integrating four operating modes into a compact structure. Two simple and effective collaborative mechanisms are introduced to coordinate between pinch and suction operation and between soft and rigid components, respectively. Through the collaboration of different modes, the HMG exhibits a competitive grasping diversity across four aspects, including weight (from 0.2 g to 10 kg), fragility (from jelly to aluminum profile), size scale (from 0.46 mm to 0.55 m), and shape (from poorly pinchable to poorly suckable). We further demonstrate its adaptability and robustness in handling irregular-shaped objects, and its proficiency in executing complex real-world manipulation tasks, underwater operations, and closed-loop grasping. Its enhanced grasping diversity is poised to accelerate diverse applications in daily life, industrial settings, and underwater scenarios.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- A Novel Iterative Solution to the Perspective-$n$-Point Problem via Cost
Function Approximation-
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Authors: Lipu Zhou;Zhenzhong Wei;Xu Wang;
Pages: 3908 - 3928
Abstract: The perspective-$n$-point (P$n$P) problem, which estimates the camera pose through $N$ 2-D/3-D point correspondences, has been extensively studied. Although minimizing the reprojection cost is regarded as the gold standard for solving the P$n$P problem, this cost lacks an analytic solution, leading previous works to focus on developing simpler costs. State-of-the-art P$n$P solutions are generally considered to be close to the gold-standard solution. However, this perception is based on limited experimental setups. Our extensive evaluations show that these solutions generally deviate from the gold-standard solution as the depth range of 3-D points increases. This article investigates two noise models of the P$n$P problem and provides a unified, accurate, and efficient solution. The main contributions of this article are threefold. First, we propose an efficient initialization method that compresses $ 2N$ constraints to three quadratic equations for rotation using principal component analysis. Second, we prove that our initialization algorithm provides a solution to the P3P problem, making it applicable to the full range $N \geq 3$ of the P$n$P problem. Third, we propose a novel iterative algorithm that approximates reprojection residuals using second-order polynomials and determines the optimal step size analytically, ensuring fast convergence. Extensive experiments on synthetic and real data demonstrate that our algorithm outperforms state-of-the-art methods in terms of accuracy and robustness, while achieving comparable efficiency.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Tracking and Control of Multiple Objects During Nonprehensile Manipulation
in Clutter-
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Authors: Zisong Xu;Rafael Papallas;Jaina Modisett;Markus Billeter;Mehmet R. Dogar;
Pages: 3929 - 3947
Abstract: This article introduces a method for 6-D pose tracking and control of multiple objects during nonprehensile manipulation by a robot. The tracking system estimates objects’ poses by integrating physics predictions, derived from robotic joint state information, with visual inputs from an RGB-D camera. Specifically, the methodology is based on particle filtering, which fuses control information from the robot as an input for each particle movement and with real-time camera observations to track the pose of objects. Comparative analyses reveal that this physics-based approach substantially improves pose tracking accuracy over baseline methods that rely solely on visual data, particularly during manipulation in clutter, where occlusions are a frequent problem. The tracking system is integrated with a model predictive control approach which shows that the probabilistic nature of our tracking system can help robust manipulation planning and control of multiple objects in clutter, even under heavy occlusions.
PubDate: FRI, 06 JUN 2025 09:16:29 -04
Issue No: Vol. 41, No. null (2025)
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- A Learning-Based Quadcopter Controller With Extreme Adaptation
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Authors: Dingqi Zhang;Antonio Loquercio;Jerry Tang;Ting-Hao Wang;Jitendra Malik;Mark W. Mueller;
Pages: 3948 - 3964
Abstract: This article introduces a learning-based low-level controller for quadcopters, which adaptively controls quadcopters with significant variations in mass, size, and actuator capabilities. Our approach leverages a combination of imitation learning and reinforcement learning, creating a fast-adapting and general control framework for quadcopters that eliminates the need for precise model estimation or manual tuning. The controller estimates a latent representation of the vehicle’s system parameters from sensor-action history, enabling it to adapt swiftly to diverse dynamics. Extensive evaluations in simulation demonstrate the controller’s ability to generalize to unseen quadcopter parameters, with an adaptation range up to 16 times broader than the training set. In real-world tests, the controller is successfully deployed on quadcopters with mass differences of 3.7 times and propeller constants varying by more than 100 times, while also showing rapid adaptation to disturbances such as off-center payloads and motor failures. These results highlight the potential of our controller to simplify the design process and enhance the reliability of autonomous drone operations in unpredictable environments.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- TacFlex: Multimode Tactile Imprints Simulation for Visuotactile Sensors
With Coating Patterns-
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Authors: Chaofan Zhang;Shaowei Cui;Jingyi Hu;Tianyu Jiang;Tiandong Zhang;Rui Wang;Shuo Wang;
Pages: 3965 - 3985
Abstract: Visuotactile sensors have been shown to provide rich contact information for robots. However, how to build a high-fidelity visuotactile simulator that supports multimode tactile imprints and various sensor configurations (such as coating patterns) remains a challenging problem. In this article, we present TacFlex, an efficient and flexible simulator for visuotactile sensors, which physically simulates the elastomer deformation using finite element methods, and focuses on linking the deformed elastomer mesh to diverse tactile imprints, including tactile images with arbitrary coating patterns and tactile 3-D point clouds. We further propose a ray tracing-based rectification method to deal with multimedium refraction effects to make the simulated tactile images more realistic. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness of TacFlex on several visuotactile sensors. Furthermore, we explore the Sim2Real performance of different tactile imprints provided by TacFlex in tactile perception and manipulation tasks, such as cylindrical object pose estimation and peg-in-hole. The perception/policy models trained in simulation are successfully deployed in the real world. Finally, we present the outlook on the potential of TacFlex in visuotactile manipulation learning. The TacFlex simulator is open-sourced to the community (https://sites.google.com/view/tacflex/).
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Rhythm-Based Power Allocation Strategy of Bionic Tail-Flapping for
Propulsion Enhancement-
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Authors: Biao Wu;Chaoyi Huang;Xiangru Li;Jiahao Xu;Sicong Liu;James Lam;Zheng Wang;Jiansheng Dai;
Pages: 3986 - 4004
Abstract: With the vast demand in marine development, robotic fish show promising potential in underwater exploration for their high-performance propulsion ability. However, fish-inspired robots are yet to utilize the structural flexibility of rhythmic actuation such as bony fish (Osteichthyes). The Body and Caudal Fin (BCF) locomotion in fish optimizes the use of muscle power and body flexibility by synchronizing muscle activation with the undulating-oscillatory tail-flapping, such as Thunniform, while robotic fish are primarily designed as motion trackers rather than as efficient swimmers. In this article, we propose a power allocation strategy (PAS) that imitates muscle rhythmic actuation, which increases the flapping amplitude by the coupling of the peduncle motion and the tail deformation. Inspired by this peduncle-tail mechanism, we developed a direct-drive fish robot (DDRFishBot). The DDRFishBot is enhanced by our developed PAS in tail-elastic potential energy release by 228%, in propulsion by 45.6%, and in efficiency coefficient by 16.3%. This study establishes the performance enhancement principle of exploiting tail flexibility through a simple scotch yoke mechanism, expanding the performance space of fish-inspired tail-flapping swimming robot.
PubDate: MON, 09 JUN 2025 09:16:26 -04
Issue No: Vol. 41, No. null (2025)
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- ROVER: A Multiseason Dataset for Visual SLAM
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Authors: Fabian Schmidt;Julian Daubermann;Marcel Mitschke;Constantin Blessing;Stephan Meyer;Markus Enzweiler;Abhinav Valada;
Pages: 4005 - 4022
Abstract: Robust simultaneous localization and mapping (SLAM) is a crucial enabler for autonomous navigation in natural, semistructured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasonal changes, varying light conditions, and dense vegetation. These factors often degrade the performance of visual SLAM algorithms originally developed for structured urban environments. To address this gap, we present robot outdoor visual SLAM dataset for environmental robustness (ROVER), a comprehensive benchmark dataset tailored for evaluating visual SLAM algorithms under diverse environmental conditions and spatial configurations. We captured the dataset with a robotic platform equipped with monocular, stereo, and RGBD cameras, as well as inertial sensors. It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios, i.e., day, dusk, and night with and without external lighting. With this novel dataset, we evaluate several traditional and deep learning-based SLAM methods and study their performance in diverse challenging conditions. The results demonstrate that while stereo-inertial and RGBD configurations generally perform better under favorable lighting and moderate vegetation, most SLAM systems perform poorly in low-light and high-vegetation scenarios, particularly during summer and autumn. Our analysis highlights the need for improved adaptability in visual SLAM algorithms for outdoor applications, as current systems struggle with dynamic environmental factors affecting scale, feature extraction, and trajectory consistency. This dataset provides a solid foundation for advancing visual SLAM research in real-world, semistructured environments, fostering the development of more resilient SLAM systems for long-term outdoor localization and mapping.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Goal-Conditioned Model Simplification for 1-D and 2-D Deformable Object
Manipulation-
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Authors: Shengyin Wang;Matteo Leonetti;Mehmet Dogar;
Pages: 4023 - 4040
Abstract: Motion planning for deformable object manipulation has been a challenge for a long time in robotics due to its high computational cost. In this work, we propose to mitigate this cost by limiting the number of picking points on a deformable object within the action space and simplifying the dynamics model. We do this first by identifying a minimal geometric model that closely approximates the original model at the goal state; specifically, we implement this general approach for 1-D linear deformable objects (e.g., ropes) using a piece-wise line-fitted model, and for 2-D surface deformable objects (e.g., cloth) using a mesh-simplified model. Then a small number of key particles are extracted as the pickable points in the action space which are sufficient to represent and reach the given goal. In addition, a simplified dynamics model is constructed based on the simplified geometric model, containing much fewer particles and thus being much faster to simulate than the original dynamics model, albeit with some loss of precision. We further refine this model iteratively by adding more details from the actually achieved final state of the original model until a satisfactory trajectory is generated. Extensive simulation experiments are conducted on a set of representative tasks for ropes and cloth, which show a significant decrease in time cost while achieving similar or better trajectory costs. Finally, we establish a closed-loop system of perception, planning, and control with a real robot for cloth folding, which validates the effectiveness of our proposed method.
PubDate: THU, 05 JUN 2025 09:16:07 -04
Issue No: Vol. 41, No. null (2025)
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- Optimal On-the-Fly Route Planning With Rich Transportation Requests
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Authors: Cristian-Ioan Vasile;Jana Tumova;Sertac Karaman;Calin Belta;Daniela Rus;
Pages: 4041 - 4056
Abstract: This article considers the route planning problem for a vehicle with limited capacity operating in a road network. The vehicle is assigned a set of transportation requests that are more complex than traveling between two locations, may involve dependencies between their subtasks, and include deadlines and priorities. The requests arrive gradually over the deployment time-horizon, and thus replanning is needed for new requests. We address cases when not all requests can be serviced by their deadlines despite car sharing. We introduce multiple quality measures for plans that account for requests’ delays with respect to deadlines and priorities. We formalize the problem as planning in a weighted transition system under syntactically cosafe LTL formulas. We develop an online planning and replanning algorithm based on the automata-based approach to least-violating plan synthesis and on translation to a mixed integer linear program (MILP). Furthermore, we show that the MILP reduces to graph search for a subclass of quality measures that satisfy a monotonicity property. We show the approach in simulations, including a case study on the mid-Manhattan road network over the span of 24 h.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously
Optimizing Robot Poses and Occupancy Map-
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Authors: Yingyu Wang;Liang Zhao;Shoudong Huang;
Pages: 4057 - 4077
Abstract: Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations because they effectively classify spaces into obstacles, free, and uknown regions, providing robots with spatial information for various tasks. In this article, we propose Occupancy-SLAM, a novel optimization-based SLAM method enabling the joint optimization of robot trajectory and the occupancy map through a parameterized map representation. The key novelty lies in optimizing both robot poses and occupancy values at different cell vertices simultaneously, a significant departure from existing methods, where the robot poses need to be optimized first before the map can be estimated. In our formulation, the state variables in optimization include both robot poses and occupancy values at cell vertices in the map. Moreover, a multi-resolution optimization framework utilizing occupancy maps with varying resolutions in different stages is introduced. A variation of GaussNewton method is proposed to solve the optimization problem at different stages. The proposed algorithm efficiently converges with initialization from odometry inputs. Furthermore, we propose an occupancy submap joining method within Occupancy-SLAM framework to handle large-scale problems effectively. Evaluations using simulations and practical 2D datasets demonstrate that the proposed approach can robustly obtain more accurate results than state-of-the-art techniques, with comparable computational time. Preliminary 3D results further confirm the potential of the proposed method in practical 3D applications, achieving more accurate results than existing methods.
PubDate: MON, 09 JUN 2025 09:16:26 -04
Issue No: Vol. 41, No. null (2025)
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- On the Passive Virtual Viscous Element Injection Method for Elastic Joint
Robots-
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Authors: Jiexin Zhang;Tengyu Hou;Ye Ding;Bo Zhang;Honghai Liu;
Pages: 4078 - 4099
Abstract: Increasing the viscosity of elastic joints can significantly improve the performance of elastic joint robots during physical human–robot interactions. However, current approaches for injecting viscous elements require an additional damper to be added in parallel with the elastic elements. In this article, we propose a new concept called virtual viscous element injection (VVI), which enables a robot to exhibit viscoelasticity without altering its mechanical structure. VVI relies only on motor-side dynamics reshaping and state feedback. Interestingly, the VVI method allows high-resolution joint torque measurements in elastic joint robots, unlike in physical viscoelastic joint robots, which measure joint torque using higher-order derivatives of the positions. Furthermore, the VVI method is proved to preserve the passivity of robot dynamics, which provides numerous possibilities for the applications of combined passivity-based controllers. Specifically, we first emphasize the impedance control method using VVI. The results demonstrate that the VVI-DF method, which combines the direct feedback method with VVI, addresses the issue of excessive acceleration feedback in the controller. This provides looser constraints for achieving a high-gain torque loop in impedance control. Moreover, this article also provides examples of the application of VVI combined with passivity-based position and torque controllers. Experiments and simulations demonstrate the effectiveness of the proposed methods. The proposed method can be extended to various robots, such as exoskeletons, and collaborative robots.
PubDate: THU, 05 JUN 2025 09:16:23 -04
Issue No: Vol. 41, No. null (2025)
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- Time, Travel, and Energy in the Uniform Dispersion Problem
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Authors: Michael Amir;Alfred M. Bruckstein;
Pages: 4100 - 4119
Abstract: We investigate the algorithmic problem of uniformly dispersing a swarm of robots in an unknown, grid-like environment. In this setting, our goal is to study the relationships between performance metrics and robot capabilities. We introduce a formal model comparing dispersion algorithms based on makespan, traveled distance, energy consumption, sensing, communication, and memory. Using this framework, we classify uniform dispersion algorithms according to their capability requirements and performance. We prove that while makespan and travel can be minimized in all environments, energy cannot, if the swarm’s sensing range is bounded. In contrast, we show that energy can be minimized by “ant-like” robots in synchronous settings and asymptotically minimized in asynchronous settings, provided the environment is topologically simply connected, by using our “find-corner depth-first search” (FCDFS) algorithm. Our theoretical and experimental results show that FCDFS significantly outperforms known algorithms. Our findings reveal key limitations in designing swarm robotics systems for unknown environments, emphasizing the role of topology in energy-efficient dispersion.
PubDate: FRI, 06 JUN 2025 09:16:12 -04
Issue No: Vol. 41, No. null (2025)
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