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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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- Quantifying the Risk of Unmapped Associations for Mobile Robot
Localization Safety-
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Authors: Yihe Chen;Boris Pervan;Matthew Spenko;
Pages: 2920 - 2931
Abstract: Integrity risk is a measure of localization safety that accounts for the presence of undetected sensor faults. The metric has been used for decades in aviation and has recently been applied to terrestrial robots operating on life-critical missions. For ground vehicles, integrity risk can be quantified for systems using lidar measurements, where two specific fault types have been identified: miss-association and unmapped association. While miss-association faults, which occur when a correctly extracted feature is associated with the wrong landmark, have been well studied, the probability of an unmapped association fault, where an incorrectly extracted feature is associated with a landmark, is not well understood. Namely, previous research has never quantified this value and instead relies on an assumed value, one whose value has not been properly justified. This work is the first to provide a methodology that estimates the risk of unmapped association for each mapped landmark; the article demonstrates the effect of this probability for both the chi-squared and fixed-lag smoothing methods for integrity monitoring. Data collected in downtown Chicago, IL, USA, were used to test the impact of unmapped association faults on localization safety. The results indicate that using the previously assumed value is reasonable in many situations, but that applications with strict safety requirements should incorporate the method described here to properly account for unmapped association faults.
PubDate: WED, 15 MAY 2024 09:15:46 -04
Issue No: Vol. 40, No. null (2024)
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- Smooth Distances for Second-Order Kinematic Robot Control
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Authors: Vinicius Mariano Gonçalves;Anthony Tzes;Farshad Khorrami;Philippe Fraisse;
Pages: 2950 - 2966
Abstract: In this article, we propose an algorithm for computing a smoothed version of the distance between two objects. As opposed to the traditional Euclidean distance between two objects, which may not be differentiable, this smoothed distance is guaranteed to be differentiable. Differentiability is an important property in many applications, in particular in robotics, in which obstacle-avoidance schemes often rely on the derivative/Jacobian of the distance between two objects. We prove mathematical properties of this smoothed distance and of the algorithm for computing it, and show its applicability in robotics by applying it to a second-order kinematic control framework, also proposed in this article. The control framework using smooth distances was successfully implemented on a 7 DOF manipulator.
PubDate: TUE, 14 MAY 2024 09:15:49 -04
Issue No: Vol. 40, No. null (2024)
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- Keypoint-Guided Efficient Pose Estimation and Domain Adaptation for Micro
Aerial Vehicles-
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Authors: Ye Zheng;Canlun Zheng;Jiahao Shen;Peidong Liu;Shiyu Zhao;
Pages: 2967 - 2983
Abstract: Visual detection of micro aerial vehicles (MAVs) is an important problem in many tasks such as vision-based swarming of MAVs. This article studies vision-based 6-D pose estimation to detect a 3-D bounding box of a target MAV, and then, estimate its 3-D position and 3-D attitude. The 3-D attitude information is critical to better estimate the target's velocity since the attitude and motion are dynamically coupled. In this article, we propose a novel 6-D pose estimation method, whose novelties are threefold. First, we propose a novel centroid point-guided keypoint localization network that outperforms the state-of-the-art methods in terms of both accuracy and efficiency. Second, while there are no publicly available real-world datasets for 6-D pose estimation for MAVs up to now, we propose a high-quality dataset based on an automatic dataset collection method. Third, since the dataset is collected in an indoor environment but detection tasks are usually in outdoor environments, we propose a self-training-based unsupervised domain adaption method to transfer the method from indoor to outdoor. Finally, we show that the estimated 6-D pose especially the 3-D attitude can significantly help improve the target's velocity estimation.
PubDate: TUE, 14 MAY 2024 09:15:49 -04
Issue No: Vol. 40, No. null (2024)
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- A Novel Contact-Aided Continuum Robotic System: Design, Modeling, and
Validation-
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Authors: Zheshuai Yang;Laihao Yang;Yu Sun;Xuefeng Chen;
Pages: 3024 - 3043
Abstract: Tendon-driven continuum robots are of great promise in dexterous manipulation in long-narrow spaces, such as in-situ maintenance of aeroengines, due to their slender body and compliant hyper-redundant architecture. However, major challenges in implementing this come from mechanical design and morphology estimation: torsion and buckling issues induced by the intrinsic compliant architecture and the coupling of system gravity and distal loads; and low-accuracy morphology model influenced by complex load conditions. In this article, inspired by the contact-aided compliant mechanisms (CACMs), a novel continuum robotic system using the bearing-based CACM is developed to overcome the two intrinsic issues (i.e., torsion and buckling) while eliminating the implied wear due to friction at joint/socket interfaces without affecting its stiffness adversely. Subsequently, based on the chained beam constraint model, a comprehensive kinetostatic modeling framework is systematically derived, focusing on mechanism-oriented strategies (i.e., tendon routing friction, physical joint constraint, and section buckling estimation). Finally, various experiments are performed to verify the effectiveness of both our designed hardware and algorithm. It is demonstrated that the robotic system with such hardware and algorithm achieving the torsional stiffness outperforms the twin-pivot design at least 24 times, stiffness enhancement> 100 times, morphology error < 2.5% of the manipulator length, and avoiding the first-order instability. Additionally, we demonstrate the navigation experiment by using two developed control strategies to show the performances of the robotic system.
PubDate: THU, 16 MAY 2024 09:15:45 -04
Issue No: Vol. 40, No. null (2024)
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- Autonomous Drone Racing: A Survey
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Authors: Drew Hanover;Antonio Loquercio;Leonard Bauersfeld;Angel Romero;Robert Penicka;Yunlong Song;Giovanni Cioffi;Elia Kaufmann;Davide Scaramuzza;
Pages: 3044 - 3067
Abstract: Over the last decade, the use of autonomous drone systems for surveying, search and rescue, or last-mile delivery has increased exponentially. With the rise of these applications comes the need for highly robust, safety-critical algorithms that can operate drones in complex and uncertain environments. In addition, flying fast enables drones to cover more ground, increasing productivity and further strengthening their use case. One proxy for developing algorithms used in high-speed navigation is the task of autonomous drone racing (ADR), where researchers program drones to fly through a sequence of gates and avoid obstacles as quickly as possible using onboard sensors and limited computational power. Speeds and accelerations exceed over 80 $\text{km}/\text{h}$ and 4 g, respectively, raising significant challenges across perception, planning, control, and state estimation. To achieve maximum performance, systems require real-time algorithms that are robust to motion blur, high dynamic range, model uncertainties, aerodynamic disturbances, and often unpredictable opponents. This survey covers the progression of ADR across model-based and learning-based approaches. In this article, we provide an overview of the field, its evolution over the years, and conclude with the biggest challenges and open questions to be faced in the future.
PubDate: TUE, 14 MAY 2024 09:15:49 -04
Issue No: Vol. 40, No. null (2024)
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- Safe Set-Based Trajectory Planning for Robotic Manipulators
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Authors: Ryan McGovern;Nikolaos Athanasopoulos;Seán McLoone;
Pages: 3082 - 3096
Abstract: Inspired by the line of seminal works on projected path dynamics and time-optimal control of robots, which originated in the 1980s, and recent advances on the computation of safe sets for complex systems in control, we present a new trajectory planning framework for $N$-link robotic manipulators. Given a path, defined typically in the workspace, we recover the admissible velocity profiles and the realizable corresponding torque profiles that achieve a path traversal. To make this possible, we introduce a new torque feedback parameterization. This enables us to construct the set where the trajectory of the projected path can be confined while reaching a target set with a feasible control action, namely, the reach–avoid set. As a product of this procedure, we develop feedback controllers that guarantee state and input constraint satisfaction, can track reference trajectories, and can handle temporal specifications related, for example, to rendezvous and avoidance setups. Encouraging proof-of-concept experimental evaluation of the theory on a UR10 robotic manipulator suggests the framework can complement and further expand the existing classical approaches.
PubDate: TUE, 14 MAY 2024 09:15:49 -04
Issue No: Vol. 40, No. null (2024)
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- Robust Visual Feedback Control for Precise In-Hand Manipulation Using
Parallel Soft Actuators-
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Authors: Yoshiki Mori;Mingzhu Zhu;Sadao Kawamura;
Pages: 3097 - 3108
Abstract: Soft robotic hands are reliable for grasping objects of various shapes. However, they perform poorly in the high-precision manipulation of grasped objects because the modeling and sensing of soft actuator deformation are complex. To overcome this problem, in a previous study, we proposed a robust visual feedback control method for precise in-hand manipulation using parallel soft actuators. This method enables precise in-hand manipulation without measuring the soft actuator deformations. Generally, in the feedback control of a parallel drive system, the actuator force is converted into the force/torque applied to the grasped object. The conversion matrix constantly changes depending on the position/orientation of the object and the contact points of the soft actuators with the object. Consequently, accurately measuring the conversion matrix is difficult. Therefore, we estimated it as a constant matrix in our previous studies, and its robustness was confirmed experimentally. However, its theoretical robustness was not analyzed sufficiently. Therefore, in this article, we discuss the robustness of the estimated constant matrix through a mathematical stability proof. Furthermore, we investigate the characteristics of the estimated drive matrix. Then, we perform a numerical robustness analysis. Finally, the robustness of the proposed method is studied via the above investigation and verification experiments.
PubDate: WED, 05 JUN 2024 09:17:20 -04
Issue No: Vol. 40, No. null (2024)
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- A Meniscus-Like Structure in Anthropomorphic Joints to Attenuate Impacts
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Authors: Lianxin Yang;Zhihua Zhao;
Pages: 3109 - 3126
Abstract: During robotic locomotion, shock forces from ground impact propagate through the leg and may cause fatigue or damage to joints and sensitive hardware. To attenuate impacts in diverse aspects and directions, multiple approaches, including active control strategies and passive compliant joints, are essential for providing a comprehensive solution. Here, inspired by human knees, a meniscus-like structure was developed for a compliant anthropomorphic joint to provide a complementary way of shock absorption, especially along the axial direction. The proposed meniscus-like structure comprises a pair of curved arms wrapped with preloaded elastic bands whose elongations produce restoring forces against the axial load. This structure simultaneously realized impact attenuation in three aspects: Decreasing contact stress by designing consistently conformal contact interfaces under axial movement; reducing peak impact forces by tuning load-displacement curves to obtain a high-static-low-dynamic nonlinear stiffness; and dissipating energy by hysteresis due to sliding frictions. The effectiveness in attenuating impacts on robotic legs was further verified by both analytical analyses and impact experiments that it outperforms regular elastic buffers at multiple leg configurations. Inserting meniscus-like structures into anthropomorphic joints efficiently utilized the joint space to attenuate axial impacts, complementing the system of interaction safety for the robot community.
PubDate: FRI, 31 MAY 2024 09:15:58 -04
Issue No: Vol. 40, No. null (2024)
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- MINRob: A Large Force-Outputting Miniature Robot Based on a Triple-Magnet
System-
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Authors: Yuxuan Xiang;Ruomao Liu;Zihan Wei;Xinliang Wang;Weida Kang;Min Wang;Jun Liu;Xudong Liang;Jiachen Zhang;
Pages: 3127 - 3145
Abstract: Magnetically actuated miniature robots are limited in their mechanical outputting capability, because the magnetic forces decrease significantly with decreasing robot size and increasing actuating distance. Hence, the output force of these robots can hardly meet the demand for specific biomedical applications (e.g., tissue penetration). This article proposes a tetherless magnetic impact needle robot (MINRob) based on a triple-magnet system with reversible and repeatable magnetic collisions to overcome this constraint on output force. The working procedure of the proposed system is divided into several states, and a mathematical model is developed to predict and optimize the force output. These force values in magnetic impact and penetration are obtained from a customized setup, indicating a ten-fold increase compared with existing miniature robots that only utilize magnetic attractive force. Eventually, the proposed MINRob is integrated with a teleoperation system, enabling remote and precise control of the robot's position and orientation. The triple-magnet system offers promising locomotion patterns and penetration capacity via the notably increased force output, showing great potential in robot-assisted tissue penetration in minimally invasive healthcare.
PubDate: WED, 05 JUN 2024 09:17:20 -04
Issue No: Vol. 40, No. null (2024)
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- Planar Friction Modeling With LuGre Dynamics and Limit Surfaces
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Authors: Gabriel Arslan Waltersson;Yiannis Karayiannidis;
Pages: 3166 - 3180
Abstract: During planar motion, contact surfaces exhibit a coupling between tangential and rotational friction forces. This article proposes planar friction models grounded in the LuGre model and limit surface theory. First, distributed planar extended state models are proposed, and the elastoplastic model is extended for multidimensional friction. Subsequently, we derive a reduced planar friction model coupled with a precalculated limit surface, which offers the reduced computational cost. The limit surface approximation through an ellipsoid is discussed. The properties of the planar friction models are assessed in various simulations, demonstrating that the reduced planar friction model achieves comparable performance to the distributed model while exhibiting $\sim\! 80$ times the lower computational cost.
PubDate: THU, 06 JUN 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- A Minimally Designed Audio-Animatronic Robot
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Authors: Kyu Min Park;Jeongah Cheon;Sehyuk Yim;
Pages: 3181 - 3198
Abstract: Animatronic robots that simulate the lively and realistic motions of creatures can be excellent robotic platforms for social interaction with people. In particular, the robot head is a very important part of expressing various emotions and generating human-friendly and aesthetic impressions. This article presents Ray, a new type of audio-animatronic robot head. All the mechanical structure of the robot is built in one step by 3-D printing and has multiple layers expressing the overall shape of a human head and important features such as eyes, nose, mouth, and chin. This simple, lightweight structure and the separatetendon-based actuation system underneath allow for smooth, fast motions of the robot. We also develop an audio-driven motion generation module that automatically synthesizes natural and rhythmic motions of the head and mouth based on the given audio. The developed robot platform is used for various applications, for example, as a talking robot, robot singer, and robot MC. We expect this research opens up a new paradigm and application possibilities for minimally designed audio-animatronic robots.
PubDate: THU, 06 JUN 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- A Cable-Driven Upper Limb Rehabilitation Robot With Muscle-Synergy-Based
Myoelectric Controller-
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Authors: Chenglin Xie;Yueling Lyu;Guoxin Li;Raymond Kai-Yu Tong;Haisheng Xia;Rong Song;Zhijun Li;
Pages: 3199 - 3211
Abstract: Surface electromyography (sEMG) signal has been used in upper limb rehabilitation robots (ULRR). However, existing ULRR based on myoelectric controllers suffers from limited generalization ability in estimating three-dimensional (3-D) motion intention. This article proposes a muscle-synergy-inspired approach to enhance the generalization ability of the myoelectric controller of a cable-driven ULRR. Low-dimensional commands are extracted from sEMG signals based on an EMG-to-muscle activation model and non-negative matrix factorization. The extracted commands are used to estimate the 3-D human force. Two different trajectory tracking tasks are selected to test the generalization ability. The system is trained based on training sets where participants perform one task. Then the system is tested using testing sets where participants perform the other task. Finally, the system is verified on real-time robotic control experiment. Results show that the proposed controller achieves better force estimating accuracy, better trajectory tracking accuracy, and lower interaction force than the myoelectric controller without considering muscle synergies, which means the proposed controller yields better generalization performance.
PubDate: MON, 10 JUN 2024 09:15:39 -04
Issue No: Vol. 40, No. null (2024)
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- Robust Quadrupedal Jumping With Impact-Aware Landing: Exploiting Parallel
Elasticity-
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Authors: Jiatao Ding;Vassil Atanassov;Edoardo Panichi;Jens Kober;Cosimo Della Santina;
Pages: 3212 - 3231
Abstract: Introducing parallel elasticity in the hardware design endows quadrupedal robots with the ability to perform explosive and efficient motions. However, for this kind of articulated soft quadruped, realizing dynamic jumping with robustness against system uncertainties remains a challenging problem. To achieve this, we propose an impact-aware jumping planning and control approach. Specifically, an offline kino-dynamic-type trajectory optimizer is first formulated to achieve compliant 3-D jumping motions, using a novel actuated spring-loaded inverted pendulum (SLIP) model. Then, an optimization-based online landing strategy, including preimpact leg motion modulation and postimpact landing recovery, is designed. The actuated SLIP model, with the capability of explicitly characterizing parallel elasticity, captures the jumping and landing dynamics, making the problem of motion generation/regulation more tractable. Finally, a hybrid torque control consisting of a feedback tracking loop and a feedforward compensation loop is employed for motion control. Experiments demonstrate the ability to accomplish robust 3-D jumping motions with stable landing and recovery. Besides, our approach can be applied to quadrupedal robots with or without additional parallel compliance.
PubDate: MON, 10 JUN 2024 09:15:39 -04
Issue No: Vol. 40, No. null (2024)
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- TossNet: Learning to Accurately Measure and Predict Robot Throwing of
Arbitrary Objects in Real Time With Proprioceptive Sensing-
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Authors: Lipeng Chen;Weifeng Lu;Kun Zhang;Yizheng Zhang;Longfei Zhao;Yu Zheng;
Pages: 3232 - 3251
Abstract: Accurate measuring and modeling of dynamic robot manipulation (e.g., tossing and catching) is particularly challenging, due to the inherent nonlinearity, complexity, and uncertainty in high-speed robot motions and highly dynamic robot–object interactions happening in very short distances and times. Most studies leverage extrinsic sensors such as visual and tactile feedback toward task or object-centric modeling of manipulation dynamics, which, however, may hit bottleneck due to the significant cost and complexity, e.g., the environmental restrictions. In this work, we investigate whether using solely the on-board proprioceptive sensory modalities can effectively capture and characterize dynamic manipulation processes. In particular, we present an object-agnostic strategy to learn the robot toss dynamics of arbitrary unknown objects from the spatio-temporal variations of robot toss movements and wrist-force/torque (F/T) observations. We then propose TossNet, an end-to-end formulation that jointly measures the robot toss dynamics and predicts the resulting flying trajectories of the tossed objects. Experimental results in both simulation and real-world scenarios demonstrate that our methods can accurately model the robot toss dynamics of both seen and unseen objects, and predict their flying trajectories with superior prediction accuracy in nearly real-time. Ablative results are also presented to demonstrate the effectiveness of each proprioceptive modality and their correlations in modeling the toss dynamics. Case studies show that TossNet can be applied on various real robot platforms for challenging tossing-centric robot applications, such as blind juggling and high-precise robot pitching.
PubDate: TUE, 18 JUN 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- Parallel-Continuum Robots: A Survey
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Authors: Sven Lilge;Kathrin Nuelle;Jake A. Childs;Kefei Wen;D. Caleb Rucker;Jessica Burgner-Kahrs;
Pages: 3252 - 3270
Abstract: Parallel-continuum robots combine the advantages of both parallel and continuum robotics. They offer a compromise between the inherent compliance and slenderness of continuum robots and the high precision and strength of rigid-link parallel robots. Throughout recent years there has been an increasing research interest in these novel architectures, which form closed kinematic chains that feature flexible, continuous links undergoing elastic deformations. As the number of publications in this emerging research field is steadily increasing, this survey article summarizes and reviews the state of the art in parallel-continuum robots, discussing their design and modeling. A definition and notation for parallel-continuum robots is introduced, allowing for a clear classification. In conclusion, current open research questions and possible applications for such robots are discussed.
PubDate: MON, 17 JUN 2024 09:16:38 -04
Issue No: Vol. 40, No. null (2024)
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- Analytical Model and Experimental Testing of the SoftFoot: An Adaptive
Robot Foot for Walking Over Obstacles and Irregular Terrains-
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Authors: Cristina Piazza;Cosimo Della Santina;Giorgio Grioli;Antonio Bicchi;Manuel G. Catalano;
Pages: 3290 - 3305
Abstract: Robot feet are crucial for maintaining dynamic stability and propelling the body during walking, especially on uneven terrains. Traditionally, robot feet were mostly designed as flat and stiff pieces of metal, which meets its limitations when the robot is required to step on irregular grounds, e.g., stones. While one could think that adding compliance under such feet would solve the problem, this is not the case. To address this problem, we introduced the SoftFoot, an adaptive foot design that can enhance walking performance over irregular grounds. The proposed design is completely passive and varies its shape and stiffness based on the exerted forces, through a system of pulley, tendons, and springs opportunely placed in the structure. This article outlines the motivation behind the SoftFoot and describes the theoretical model which led to its final design. The proposed system has been experimentally tested and compared with two analogous conventional feet, a rigid one and a compliant one, with similar footprints and soles. The experimental validation focuses on the analysis of the standing performance, measured in terms of the equivalent support surface extension and the compensatory ankle angle, and the rejection of impulsive forces, which is important in events such as stepping on unforeseen obstacles. Results show that the SoftFoot has the largest equivalent support surface when standing on obstacles, and absorbs impulsive loads in a way almost as good as a compliant foot.
PubDate: MON, 17 JUN 2024 09:16:39 -04
Issue No: Vol. 40, No. null (2024)
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- An Underactuated Active Transfemoral Prosthesis With Series Elastic
Actuators Enables Multiple Locomotion Tasks-
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Authors: Ilaria Fagioli;Francesco Lanotte;Tommaso Fiumalbi;Andrea Baldoni;Alessandro Mazzarini;Filippo Dell'Agnello;Huseyin Eken;Vito Papapicco;Tommaso Ciapetti;Alessandro Maselli;Claudio Macchi;Sofia Dalmiani;Angelo Davalli;Emanuele Gruppioni;Emilio Trigili;Simona Crea;Nicola Vitiello;
Pages: 3306 - 3321
Abstract: Robotic lower limb prostheses have the power to revolutionize mobility by enhancing gait efficiency and facilitating movement. While several design approaches have been explored to create lightweight and energy-efficient devices, the potential of underactuation remains largely untapped in lower limb prosthetics. Taking inspiration from the natural harmony of walking, in this article, we have developed an innovative active transfemoral prosthesis. By incorporating underactuation, our design uses a single power actuator placed near the knee joint and connected to a differential mechanism to drive both the knee and ankle joints. We conduct comprehensive benchtop tests and evaluate the prosthesis with three individuals who have above-knee amputations, assessing its performance in walking, stair climbing, and transitions between sitting and standing. Our evaluation focuses on gathering position and torque data recorded from sensors integrated into the prosthesis and comparing these measurements to biomechanical data of able-bodied locomotion. Our findings highlight the promise of underactuation in advancing lower limb prosthetics and demonstrate the feasibility of our knee–ankle underactuated design in various tasks, showcasing its ability to replicate natural movement.
PubDate: MON, 17 JUN 2024 09:16:38 -04
Issue No: Vol. 40, No. null (2024)
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- Toward Robust Robot 3-D Perception in Urban Environments: The UT Campus
Object Dataset-
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Authors: Arthur Zhang;Chaitanya Eranki;Christina Zhang;Ji-Hwan Park;Raymond Hong;Pranav Kalyani;Lochana Kalyanaraman;Arsh Gamare;Arnav Bagad;Maria Esteva;Joydeep Biswas;
Pages: 3322 - 3340
Abstract: We introduce the UT Campus Object Dataset (CODa), a mobile robot egocentric perception dataset collected on the University of Texas Austin Campus. Our dataset contains 8.5 h of multimodal sensor data from 3-D light detection and ranging (LiDAR), stereo RGB and rgb and depth (RGBD) cameras, and a 9-DoF inertial measurement unit (IMU). CODa contains 58 min of ground truth annotations containing 1.3 million 3-D bounding boxes with instance identifiers (ID) for 53 semantic classes, 5000 frames of 3-D semantic annotations for urban terrain, and pseudoground truth localization. We repeatedly traverse identical geographic regions for diverse indoor and outdoor areas, weather conditions, and times of the day. Using CODa, we empirically demonstrate that: 1) 3-D object detection performance improves in urban settings when trained using CODa compared with existing datasets, 2) sensor-specific fine-tuning increases 3-D object detection accuracy, and 3) pretraining on CODa improves cross-dataset 3-D object detection performance in urban settings compared with pretraining on AV datasets. We release benchmarks for 3-D object detection and 3-D semantic segmentation, with future plans for additional tasks. We publicly release CODa on the Texas Data Repository (Zhang et al., 2023), pretrained models, dataset development package, and interactive dataset viewer. We expect CODa to be a valuable dataset for egocentric perception and planning for navigation in urban environments.
PubDate: TUE, 14 MAY 2024 09:15:48 -04
Issue No: Vol. 40, No. null (2024)
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- Quadratic Programming-Based Reference Spreading Control for Dual-Arm
Robotic Manipulation With Planned Simultaneous Impacts-
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Authors: Jari van Steen;Gijs van den Brandt;Nathan van de Wouw;Jens Kober;Alessandro Saccon;
Pages: 3341 - 3355
Abstract: With the aim of further enabling the exploitation of intentional impacts in robotic manipulation, a control framework is presented that directly tackles the challenges posed by tracking control of robotic manipulators that are tasked to perform nominally simultaneous impacts. This framework is an extension of the reference spreading (RS) control framework, in which overlapping ante- and post-impact references that are consistent with impact dynamics are defined. In this work, such a reference is constructed starting from a teleoperation-based approach. By using the corresponding ante- and post-impact control modes in the scope of a quadratic programming control approach, peaking of the velocity error and control inputs due to impacts is avoided while maintaining high tracking performance. With the inclusion of a novel interim mode, we aim to also avoid input peaks and steps when uncertainty in the environment causes a series of unplanned single impacts to occur rather than the planned simultaneous impact. This work in particular presents for the first time an experimental evaluation of RS control on a robotic setup, showcasing its robustness against uncertainty in the environment compared to three baseline control approaches.
PubDate: FRI, 28 JUN 2024 09:16:28 -04
Issue No: Vol. 40, No. null (2024)
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- Task and Motion Planning for Execution in the Real
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Authors: Tianyang Pan;Rahul Shome;Lydia E. Kavraki;
Pages: 3356 - 3371
Abstract: Task and motion planning represents a powerful set of hybrid planning methods that combine reasoning over discrete task domains and continuous motion generation. Traditional reasoning necessitates task domain models and enough information to ground actions to motion planning queries. Gaps in this knowledge often arise from sources such as occlusion or imprecise modeling. This work generates task and motion plans that include actions cannot be fully grounded at planning time. During execution, such an action is handled by a provided human-designed or learned closed-loop behavior. Execution combines offline planned motions and online behaviors till reaching the task goal. Failures of behaviors are fed back as constraints to find new plans. Forty real-robot trials and motivating demonstrations are performed to evaluate the proposed framework and compare it against state-of-the-art. Results show faster execution time, less number of actions, and more success in problems where diverse gaps arise. The experiment data are shared for researchers to simulate these settings. The work shows promise in expanding the applicable class of realistic partially grounded problems that robots can address.
PubDate: MON, 24 JUN 2024 09:15:42 -04
Issue No: Vol. 40, No. null (2024)
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- How Safe Is Particle Filtering-Based Localization for Mobile Robots' An
Integrity Monitoring Approach-
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Authors: Osama Abdul Hafez;Mathieu Joerger;Matthew Spenko;
Pages: 3372 - 3387
Abstract: Deriving safe bounds on particle filter estimate is a research problem that, if solved, could greatly benefit robots in life-critical applications, a field that is facing increasing interest as more robots are being deployed near humans. In response, this article introduces a new fault detector and derives a performance measure for particle filter: integrity risk. Integrity risk is defined as the probability of having large estimate errors without triggering an alarm, all while considering measurement faults, unknown deterministic errors that cannot be modeled via normal white noise. In this work, the faults come in the form of incorrectly associated features when using the local nearest neighbors. Simulations and experiments assess the efficiency of the introduced safety metric. The results show that safety improves as map density increases as long as the number of particles is sufficient to shape the error distribution and the landmarks are well separated. Also, the results indicate that, when landmarks are poorly separated, particle filter is safer than Kalman filter, whereas, when landmarks are well separated, particle filter is often, but not always, safer than Kalman filter.
PubDate: FRI, 28 JUN 2024 09:16:30 -04
Issue No: Vol. 40, No. null (2024)
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- Transferring Grasping Across Grippers: Learning–Optimization Hybrid
Framework for Generalized Planar Grasp Generation-
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Authors: Xianli Wang;Qingsong Xu;
Pages: 3388 - 3405
Abstract: As diverse robotic hands keep emerging for industrial and household use, designing general grasp synthesis algorithms applicable to multiple grippers remains challenging. To improve the generality and effectiveness of multigripper planar grasping algorithms, we propose a grasping framework featuring gripper-agnostic scene inference and gripper-changeable optimization. In our approach, we introduce an interaction probability map that bridges the scene inference and grasp optimization modules. It efficiently decouples the learning of grasping knowledge and modeling of gripper's kinematics. The inference module adopts a modified directional ensemble method with a generated fingertip dataset to refine scene information. In grasp optimization, we formulate gripper-kinematic constraints for different grippers according to joint types. Extensive evaluations on the Cornell Grasping Dataset (with a success rate of 95.51%) and on multifingered grippers (ten grippers in the real world) demonstrate that our hybrid approach generalizes learnable knowledge across various grippers. This work enables the direct transfer of learned grasping knowledge to new grippers in real-world applications.
PubDate: TUE, 02 JUL 2024 09:17:16 -04
Issue No: Vol. 40, No. null (2024)
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- Determination of All Stable and Unstable Equilibria for Image-Point-Based
Visual Servoing-
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Authors: Alessandro Colotti;Jorge García Fontán;Alexandre Goldsztejn;Sébastien Briot;François Chaumette;Olivier Kermorgant;Mohab Safey El Din;
Pages: 3406 - 3424
Abstract: Local minima are a well-known drawback of image-based visual servoing systems. Up to now, there were no formal guarantees on their number, or even their existence, according to the considered configuration. In this work, a formal approach is presented for the exhaustive computation of all minima and unstable equilibria for a class of six well-known image-based visual servoing controllers. This approach relies on a new polynomial formulation of the equilibrium condition that avoids using the camera pose. By using modern computational algebraic geometry methods and an ad hoc symmetry breaking strategy, the formal resolution of this new equilibrium condition is rendered computationally feasible. The proposed methodology is applied to compute the equilibria of several classical visual servoing tasks, with planar and nonplanar configurations of four and five points. The effects of local minima and saddle points on the dynamics of the system are finally illustrated through intensive simulation results, as well as the effects of image noise and uncertainties on depths.
PubDate: TUE, 02 JUL 2024 09:17:21 -04
Issue No: Vol. 40, No. null (2024)
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- Robust Pivoting Manipulation Using Contact Implicit Bilevel Optimization
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Authors: Yuki Shirai;Devesh K. Jha;Arvind U. Raghunathan;
Pages: 3425 - 3444
Abstract: Generalizable manipulation requires that robots be able to interact with novel objects and environment. This requirement makes manipulation extremely challenging as a robot has to reason about complex frictional interactions with uncertainty in physical properties of the object and the environment. In this article, we study robust optimization for planning of pivoting manipulation in the presence of uncertainties. We present insights about how friction can be exploited to compensate for inaccuracies in the estimates of the physical properties during manipulation. Under certain assumptions, we derive analytical expressions for stability margin provided by friction during pivoting manipulation. This margin is then used in a contact implicit bilevel optimization framework to optimize a trajectory that maximizes this stability margin to provide robustness against uncertainty in several physical parameters of the object. We present analysis of the stability margin with respect to several parameters involved in the underlying bilevel optimization problem. We demonstrate our proposed method using a 6 DoF manipulator for manipulating several different objects. We also design and validate an MPC controller using the proposed algorithm which can track and regulate the position of the object during manipulation.
PubDate: TUE, 02 JUL 2024 09:17:16 -04
Issue No: Vol. 40, No. null (2024)
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- $D^{2}$SLAM: Decentralized and Distributed Collaborative Visual-Inertial
SLAM System for Aerial Swarm-
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Authors: Hao Xu;Peize Liu;Xinyi Chen;Shaojie Shen;
Pages: 3445 - 3464
Abstract: Collaborative simultaneous localization and mapping (CSLAM) is essential for autonomous aerial swarms, laying the foundation for downstream algorithms, such as planning and control. To address existing CSLAM systems' limitations in relative localization accuracy, crucial for close-range UAV collaboration, this article introduces $D^{2}$SLAM—a novel decentralized and distributed CSLAM system. $D^{2}$SLAM innovatively manages near-field estimation for precise relative state estimation in proximity and far-field estimation for consistent global trajectories. Its adaptable front-end supports both stereo and omnidirectional cameras, catering to various operational needs and overcoming field-of-view challenges in aerial swarms. Experiments demonstrate $D^{2}$SLAM's effectiveness in accurate ego-motion estimation, relative localization, and global consistency. Enhanced by distributed optimization algorithms, $D^{2}$SLAM exhibits remarkable scalability and resilience to network delays, making it well suited for a wide range of real-world aerial swarm applications. We believe the adaptability and proven performance of $D^{2}$SLAM signify a notable advancement in autonomous aerial swarm technology.
PubDate: TUE, 02 JUL 2024 09:17:23 -04
Issue No: Vol. 40, No. null (2024)
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- Deformable Open-Frame Cable-Driven Parallel Robots: Modeling, Analysis,
and Control-
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Authors: Arthur Ngo Foon Chan;Wuichung Cheng;Darwin Lau;
Pages: 3465 - 3480
Abstract: This article proposes a generalized type of cable-driven parallel robot with deformable frames (D-CDPRs). The class of D-CDPRs allows: first, inevitable deformation of traditional rigid frame CDPRs to be considered; and second, new possibilities to develop CDPRs with lightweight frames that would deform. Comparatively, such lightweight CDPRs are easier to set up and largely reduce the cost of material and construction. However, the analysis and control of D-CDPRs are challenging as existing works usually assume the CDPR frame is rigid, such that the cable exit points on the frame are known and fixed. If the modeling errors induced by the deformable frame are not addressed appropriately, the control performance of D-CDPRs will be inaccurate and even unstable. To tackle this problem, novel modeling, analysis, and control approaches are proposed accordingly for D-CDPRs. Using the Euler–Bernoulli beam equations to develop a D-CDPR model, the workspace analysis is proposed and explored. Furthermore, the model-based feedforward length (MBFL) controller is proposed, where it is shown that cable length can be used to execute the tension control for D-CDPRs. Finally, the proposed work is validated in both simulation and hardware experiments.
PubDate: FRI, 28 JUN 2024 09:16:30 -04
Issue No: Vol. 40, No. null (2024)
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- Obstacle-Aided Trajectory Control of a Quadrupedal Robot Through
Sequential Gait Composition-
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Authors: Haodi Hu;Feifei Qian;
Pages: 3481 - 3495
Abstract: Modeling and controlling legged robot locomotion on terrains with densely distributed large rocks and boulders are fundamentally challenging. Unlike traditional methods, which often consider these rocks and boulders as obstacles and attempt to find a clear path to circumvent them, in this study, we aim to develop methods for robots to actively utilize interaction forces with these “obstacles” for locomotion and navigation. To do so, we studied the locomotion of a quadrupedal robot as it traversed a simplified obstacle field with 12 different gaits and discovered that with each gait, the robot could passively converge to a distinct orientation. A compositional return map explained this observed passive convergence and enabled prediction of the steady-state orientation angles for each quadrupedal gait. We experimentally demonstrated that with these predictions, a legged robot could effectively generate the desired shape of trajectories among large, slippery obstacles, simply by switching between different gaits. Our study offered a novel method for robots to exploit traditionally-considered “obstacles” to achieve agile movements on challenging terrains.
PubDate: THU, 06 JUN 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- A Tree-Based Next-Best-Trajectory Method for 3-D UAV Exploration
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Authors: Björn Lindqvist;Akash Patel;Kalle Löfgren;George Nikolakopoulos;
Pages: 3496 - 3513
Abstract: This work presents a fully integrated tree-based combined exploration-planning algorithm: exploration-rapidly-exploring random trees (RRT) (ERRT). The algorithm is focused on providing real-time solutions for local exploration in a fully unknown and unstructured environment while directly incorporating exploratory behavior, robot-safe path planning, and robot actuation into the central problem. ERRT provides a complete sampling and tree-based solution for evaluating “where to go next” by considering a tradeoff between maximizing information gain and minimizing the distances traveled and the robot actuation along the path. The complete scheme is evaluated in extensive simulations, comparisons, and real-world field experiments in constrained and narrow subterranean and GPS-denied environments. The framework is fully robot operating system (ROS) integrated and straightforward to use.
PubDate: WED, 03 JUL 2024 09:16:35 -04
Issue No: Vol. 40, No. null (2024)
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- Exploiting Trust for Resilient Hypothesis Testing With Malicious Robots
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Authors: Matthew Cavorsi;Orhan Eren Akgün;Michal Yemini;Andrea J. Goldsmith;Stephanie Gil;
Pages: 3514 - 3536
Abstract: In this article, we develop a resilient binary hypothesis testing framework for decision making in adversarial multirobot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a centralized fusion center (FC) even when, first, there exist malicious robots in the network and their number may be larger than the number of legitimate robots, and second, the FC uses one-shot noisy measurements from all robots. We derive two algorithms to achieve this. The first is the two-stage approach (2SA) that estimates the legitimacy of robots based on received trust observations, and provably minimizes the probability of detection error in the worst-case malicious attack. For the 2SA, we assume that the proportion of malicious robots is known but arbitrary. For the case of an unknown proportion of malicious robots, we develop the adversarial generalized likelihood ratio test (A-GLRT) that uses both the reported robot measurements and trust observations to simultaneously estimate the trustworthiness of robots, their reporting strategy, and the correct hypothesis. We exploit particular structures in the problem to show that this approach remains computationally tractable even with unknown problem parameters. We deploy both algorithms in a hardware experiment where a group of robots conducts crowdsensing of traffic conditions subject to a Sybil attack on a mock-up road network. We extract the trust observations for each robot from communication signals, which provide statistical information on the uniqueness of the sender. We show that even when the malicious robots are in the majority, the FC can reduce the probability of detection error to 30.5% and 29% for the 2SA and the A-GLRT algorithms, respectively.
PubDate: MON, 17 JUN 2024 09:16:39 -04
Issue No: Vol. 40, No. null (2024)
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- Measurement Simplification in $\rho$-POMDP with Performance Guarantees
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Authors: Tom Yotam;Vadim Indelman;
Pages: 3537 - 3550
Abstract: Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision-making problem is exponential in the action and observation spaces, thus rendering it unfeasible for many online systems. This article introduces a novel approach to efficient decision making, by partitioning the high-dimensional observation space. Using the partitioned observation space, we formulate analytical bounds on the expected information-theoretic reward, for general belief distributions. These bounds are then used to plan efficiently while maintaining performance guarantees. We show that the bounds are adaptive and computationally efficient, and that they converge to the original solution. We extend the partitioning paradigm and present a hierarchy of partitioned spaces that allows greater efficiency in planning. We then propose a specific variant of these bounds for Gaussian beliefs and show a theoretical performance improvement of at least a factor of 4. Finally, we compare our novel method to other state-of-the-art algorithms in active simultaneous localization and mapping scenarios, in simulation and in real experiments. In both cases, we show a significant speedup in planning with performance guarantees.
PubDate: FRI, 05 JUL 2024 09:15:42 -04
Issue No: Vol. 40, No. null (2024)
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- Robotic Gas Source Localization With Probabilistic Mapping and Online
Dispersion Simulation-
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Authors: Pepe Ojeda;Javier Monroy;Javier Gonzalez-Jimenez;
Pages: 3551 - 3564
Abstract: Gas source localization (GSL) with an autonomous robot is a problem with many prospective applications, from finding pipe leaks to emergency-response scenarios. In this work, we present a new method to perform GSL in realistic indoor environments, featuring obstacles, and turbulent flow. Given the highly complex relationship between the source position and the measurements available to the robot (the single-point gas concentration, and the wind vector) we propose an observation model that derives from contrasting the online, real-time simulation of the gas dispersion from any candidate source localization against a gas concentration map built from sensor readings. To account for a convenient and grounded integration of both into a probabilistic estimation framework, we introduce the concept of probabilistic gas-hit maps, which provide a higher level of abstraction to model the time-dependent nature of gas dispersion. Results from both simulated and real experiments show the capabilities of our current proposal to deal with source localization in complex indoor environments.
PubDate: WED, 10 JUL 2024 09:16:29 -04
Issue No: Vol. 40, No. null (2024)
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- Precise Control of Soft Robots Amidst Uncertain Environmental Contacts and
Forces-
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Authors: Xinjia Huang;Zihao Yuan;Xinyu Yang;Guoying Gu;
Pages: 3565 - 3580
Abstract: Recent studies have reported on the remarkable ability of bioinspired soft robots to exhibit dexterous and contact-friendly motions. However, for these robots with deformable bodies, it is extremely challenging to achieve precise and robust control when undergoing uncertain forces and contact in the environment. In this work, we take a first step to address this issue for slender pneumatic soft robots by proposing a comprehensive modeling and control framework. Our framework employs a fully parametrized model that accurately describes both robot configurations and distributed forces using Hermite interpolation. Leveraging this model, we further establish an estimation algorithm that can infer complete robot configurations and distributed external forces from limited motion data, enabling perception of contact locations and forces. Integrating this model and estimator, our control framework achieves precise robot motion control under diverse forces, with the average trajectory tracking error within 0.3 mm. It also detects and adapts to uncertain contact, demonstrated in tests of automatic obstacle avoidance and precise grasping. This framework holds promise for various applications such as environmental exploration and safe manipulation, where compliant interaction with the environment is required.
PubDate: MON, 15 JUL 2024 09:16:27 -04
Issue No: Vol. 40, No. null (2024)
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- Disturbance-Adaptive Tapered Soft Manipulator With Precise Motion
Controller for Enhanced Task Performance-
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Authors: Xianglong Li;Quan Xiong;Dongbao Sui;Qinghua Zhang;Hongwu Li;Ziqi Wang;Tianjiao Zheng;Hesheng Wang;Jie Zhao;Yanhe Zhu;
Pages: 3581 - 3601
Abstract: The field of soft manipulators requires a more promising solution, including efficient structures and controllers. This article presents a novel cable–pneumatic hybrid-driven tapered soft manipulator (TSM) design and control scheme to enhance the performance in actual tasks. This article is the first to present the design with a Bowden tube as a driving tendon and propose a composite tendon with Bowden tubes and cable tendons (BTCTs). Leveraging the principles of hybrid-driven antagonism, the compact TSM integrates the composite tendon with BTCTs and pneumatically actuated tapered bellows. This new hybrid-driven form provides the TSM with excellent resistance to axial extension, tangential bending, and torsion, enhancing the stiffness of the TSM. The variable-stiffness range of the TSM was quantified in tests, including axial stiffness (0.57–10.77 N/mm), tangential bending stiffness (0.01–0.45 N/mm), and torsion stiffness (0.02–0.044 N $\cdot$ m/$^\circ$) tests. A deep learning-based neural network approach was utilized to model the inverse kinematics of the TSM. For more precise motion control, using position and orientation feedback from the sensor at the tip, we have designed a closed-loop iterative feedback controller incorporating three algorithms. Experiments on spatial point positioning, trajectory tracking with different constraints, orientation control, and disturbance experiments were conducted on the TSM. Experimental results [spatial point positioning error (mean error of stable region: 0.17 mm), circular trajectory tracking error (mean and standard deviation (SD) of 100 trials: 0.87 $\pm$ 0.57 mm), orientation control error (less than 1$^{\circ }$), and the performance in disturbance experiment] demonstrated that our approach has high control accuracy and strong robustness against external disturbances. We conducted experiments involving teleoperation control, collision-free precise operations in cluttered and constrained environments, and disturbance-adaptive board cleaning testing, ensuring both stability and safety during contact with humans. These experiments intuitively demonstrate the potential of this TSM for executing complex tasks in real-world environments, promising to become a safe collaborative assistant for humans in the future.
PubDate: FRI, 28 JUN 2024 09:16:28 -04
Issue No: Vol. 40, No. null (2024)
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- Constrained Stein Variational Trajectory Optimization
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Authors: Thomas Power;Dmitry Berenson;
Pages: 3602 - 3619
Abstract: In this article, we present constrained Stein variational trajectory optimization (CSVTO), an algorithm for performing trajectory optimization with constraints on a set of trajectories in parallel. We frame constrained trajectory optimization as a novel form of constrained functional minimization over trajectory distributions, which avoids treating the constraints as a penalty in the objective and allows us to generate diverse sets of constraint-satisfying trajectories. Our method uses Stein variational gradient descent to find a set of particles that approximates a distribution over low-cost trajectories while obeying constraints. CSVTO is applicable to problems with differentiable equality and inequality constraints and includes a novel particle resampling step to escape local minima. By explicitly generating diverse sets of trajectories, CSVTO is better able to avoid poor local minima and is more robust to initialization. We demonstrate that CSVTO outperforms baselines in challenging highly constrained tasks, such as a 7-DoF wrench manipulation task, where CSVTO outperforms all baselines both in success and constraint satisfaction.
PubDate: MON, 15 JUL 2024 09:16:26 -04
Issue No: Vol. 40, No. null (2024)
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- Directional Critical Load Index: A Distance-to-Instability Metric for
Continuum Robots-
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Authors: Federico Zaccaria;Edoardo Idà;Sébastien Briot;
Pages: 3620 - 3637
Abstract: Equilibrium stability assessment is a primary issue in continuum robots (CRs). The possible stable-to-unstable transitions that CRs may admit complicate the use of CRs in tasks where safety and human–robot interactions are mandatory. In this context, metrics measuring the distance from instability are essential but rarely developed. Existing metrics are frequently based on the evaluation of matrices involving mixed units, thus resulting in unit-dependent metrics. Moreover, the physical meaning of existing metric is hard to interpretate. This article proposes to use the magnitude of a force that brings instability to the CR equilibrium as a measure of the distance to instability. The major advantages of this metric are the intrinsic physical meaning, the practical interpretation of the results, and the well-defined unit of the measurements. The proposed metric (named directional critical load index) is based on the linearization of the eigenvalues of the reduced Hessian matrix of the total potential energy, which can be achieved regardless of the employed discretization technique. Three different case studies illustrate and demonstrate the main results of this article.
PubDate: MON, 15 JUL 2024 09:16:27 -04
Issue No: Vol. 40, No. null (2024)
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- Reconciling RaiSim With the Maximum Dissipation Principle
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Authors: Quentin Le Lidec;Justin Carpentier;
Pages: 3638 - 3641
Abstract: Recent progress in reinforcement learning (RL) in robotics has been obtained by training control policy directly in simulation. Particularly in the context of quadrupedal locomotion, astonishing locomotion policies depicting high robustness against environmental perturbations have been trained by leveraging RaiSim simulator. While it avoids introducing forces at distance, it has been shown recently that RaiSim does not obey the maximum dissipation principle, a fundamental principle when simulating rigid contact interactions. In this note, we detail these relaxations and propose an algorithmic correction of the RaiSim contact algorithm to handle the maximum dissipation principle adequately. Our experiments empirically demonstrate our approach leads to simulation following this fundamental principle.
PubDate: THU, 18 JUL 2024 09:16:32 -04
Issue No: Vol. 40, No. null (2024)
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- High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered
Environments-
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Authors: Charbel Toumieh;Dario Floreano;
Pages: 3642 - 3656
Abstract: Coordinated flight of multiple drones allows to achieve tasks faster such as search and rescue and infrastructure inspection. Thus, pushing the State-of-the-Art of aerial swarms in navigation speed and robustness is of tremendous benefit. In particular, being able to account for unexplored/unknown environments when planning trajectories allows for safer flight. In this work, we propose the first high-speed, decentralized, and synchronous motion planning framework (HDSM) for an aerial swarm that explicitly takes into account the unknown/undiscovered parts of the environment. The proposed approach generates an optimized trajectory for each planning agent that avoids obstacles and other planning agents while moving and exploring the environment. The only global information that each agent has is the target location. The generated trajectory is high-speed, safe from unexplored spaces, and brings the agent closer to its goal. The proposed method outperforms four recent state-of-the-art methods in success rate (100% success in reaching the target location), flight speed (97% faster), and flight time (50% lower). Finally, the method is validated on a set of Crazyflie nano-drones as a proof of concept.
PubDate: TUE, 16 JUL 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- Impact Robustness Versus Torque Bandwidth: A Design Guide for Differential
Elastic Actuators-
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Authors: Anton Shu;Clara Raschel;Manuel Keppler;Armin Wedler;Martin Görner;
Pages: 3657 - 3675
Abstract: Differential elastic actuators connect a motor and a spring via differential gears to a shared output shaft, offering a more compact solution for creating mechanically robust systems than series elastic actuators, with superior torque transmission at high frequencies. The key to maximizing DEA performance lies in the careful selection of stiffness, inertia, and damping values to meet specific requirements for performance and durability. We introduce a DEA design guide that utilizes open-loop torque-bandwidth for performance evaluation and the magnitude of impact-induced gear torque for robustness evaluation. This approach enables determining DEA parameters using closed-form equations, eliminating the need for simulations or extensive expert knowledge. The effectiveness of our method is confirmed through experiments with a reconfigurable DEA prototype.
PubDate: TUE, 02 JUL 2024 09:17:21 -04
Issue No: Vol. 40, No. null (2024)
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- Automated Microrobotic Manipulation Using Reconfigurable Magnetic
Microswarms-
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Authors: Jialin Jiang;Lidong Yang;Bo Hao;Tiantian Xu;Xinyu Wu;Li Zhang;
Pages: 3676 - 3694
Abstract: Untethered microrobots possess a promising perspective for micromanipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, to achieve which the mechanism of picking, transporting, and releasing behaviors needs further investigation. In this article, we propose to achieve automated microrobotic manipulation using magnetic microswarms with multimodal morphology. The microswarm is composed of around 11–21 million $\text{Fe}_{3}\mathrm{O}_{4}$ nanoparticles (1.0$\text{--}1.8\,\mu$L particle suspension). When exposed to different dynamic magnetic fields, the swarm could exhibit corresponding forms. We realize precise and controllable cargo picking and releasing by exploiting the fluid fields of different swarm forms. In order to quantitatively describe these behaviors, we design a finite-state machine. A super-twisting sliding-mode controller has been formulated for the motion control of swarms. The disturbances are compensated via a disturbance observer. To enable automated micromanipulation in obstructed scenarios, a path planner inspired by rapidly exploring random tree algorithm is designed for path planning when obstacles exist. We also propose an enhanced-genetic algorithm to optimally transport multiple objects to the target position. Experiments demonstrate that our method could effectively transport micro-objects with different sizes and shapes. The precise selectivity of the method is validated when multiple objects exist in the working environment. Finally, the long-distance delivery ability and adaptivity to various friction situations of our strategy are demonstrated. This work explores a concise, untethered, and automated micromanipulation strategy, provides a new automatic tool for micromanipulation tasks, and extends the application potential of swarm microrobotics.
PubDate: MON, 15 JUL 2024 09:16:26 -04
Issue No: Vol. 40, No. null (2024)
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- Port-Hamiltonian Neural ODE Networks on Lie Groups for Robot Dynamics
Learning and Control-
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Authors: Thai Duong;Abdullah Altawaitan;Jason Stanley;Nikolay Atanasov;
Pages: 3695 - 3715
Abstract: Accurate models of robot dynamics are critical for safe and stable control and generalization to novel operational conditions. Hand-designed models, however, may be insufficiently accurate, even after careful parameter tuning. This motivates the use of machine learning techniques to approximate the robot dynamics over a training set of state-control trajectories. The dynamics of many robots are described in terms of their generalized coordinates on a matrix Lie group, e.g., on $\text{SE}(3)$ for ground, aerial, and underwater vehicles, and generalized velocity, and satisfy conservation of energy principles. This article proposes a port-Hamiltonian formulation over a Lie group of the structure of a neural ordinary differential equation (ODE) network to approximate the robot dynamics. In contrast to a black-box ODE network, our formulation embeds energy conservation principle and Lie group's constraints in the dynamics model and explicitly accounts for energy-dissipation effect such as friction and drag forces in the dynamics model. We develop energy shaping and damping injection control for the learned, potentially under-actuated Hamiltonian dynamics to enable a unified approach for stabilization and trajectory tracking with various robot platforms.
PubDate: MON, 15 JUL 2024 09:16:26 -04
Issue No: Vol. 40, No. null (2024)
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- Contact Models in Robotics: A Comparative Analysis
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Authors: Quentin Le Lidec;Wilson Jallet;Louis Montaut;Ivan Laptev;Cordelia Schmid;Justin Carpentier;
Pages: 3716 - 3733
Abstract: Physics simulation is ubiquitous in robotics. Whether in model-based approaches (e.g., trajectory optimization), or model-free algorithms (e.g., reinforcement learning), physics simulators are a central component of modern control pipelines in robotics. Over the past decades, several robotic simulators have been developed, each with dedicated contact modeling assumptions and algorithmic solutions. In this article, we survey the main contact models and the associated numerical methods commonly used in robotics for simulating advanced robot motions involving contact interactions. In particular, we recall the physical laws underlying contacts and friction (i.e., Signorini condition, Coulomb's law, and the maximum dissipation principle), and how they are transcribed in current simulators. For each physics engine, we expose their inherent physical relaxations along with their limitations due to the numerical techniques employed. Based on our study, we propose theoretically grounded quantitative criteria on which we build benchmarks assessing both the physical and computational aspects of simulation. We support our work with an open-source and efficient C++ implementation of the existing algorithmic variations. Our results demonstrate that some approximations or algorithms commonly used in robotics can severely widen the reality gap and impact target applications. We hope this work will help motivate the development of new contact models, contact solvers, and robotic simulators in general, at the root of recent progress in motion generation in robotics.
PubDate: FRI, 26 JUL 2024 09:16:59 -04
Issue No: Vol. 40, No. null (2024)
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- A Consistent Parallel Estimation Framework for Visual-Inertial SLAM
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Authors: Zheng Huai;Guoquan Huang;
Pages: 3734 - 3755
Abstract: In this article, we revisit the optimal fusion of visual and inertial information from a monocular camera and an inertial measurement unit and propose a novel parallel visual-inertial simultaneous localization and mapping (SLAM) estimation framework in favor of the multithread computation on a single CPU. We start modeling the SLAM problem with a Bayesian batch estimator, and then split it into two submodules, localization and mapping, of different scales and processing rates, however, can thus run concurrently. The estimation consistency is taken into account in decoupling the two submodules so that when loop closure occurs the localization accuracy can seamlessly benefit from the mapping result via online global optimization, which distinguishes our solution from the others. To this end, we design the corresponding front-end and back-end to consistently solve localization and mapping in parallel, especially the hybrid robocentric and world-centric formulations are used for modeling the respective problems. We also demonstrate the effectiveness of the proposed method using both the synthetic data generated for Monte-Carlo simulations and diverse real datasets acquired in highly-dynamic, long-term, and large-scale SLAM scenarios. Simulation results validate the significantly improved consistency and accuracy by applying our method. Experimental results show the better (competitive at least) performance against a state-of-the-art method, while being capable of processing a huge amount of measurements in building large-scale maps without blocking the high-accuracy real-time localization outputs.
PubDate: THU, 25 JUL 2024 09:16:18 -04
Issue No: Vol. 40, No. null (2024)
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- EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road
Autonomy-
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Authors: Xiaoyi Cai;Siddharth Ancha;Lakshay Sharma;Philip R. Osteen;Bernadette Bucher;Stephen Phillips;Jiuguang Wang;Michael Everett;Nicholas Roy;Jonathan P. How;
Pages: 3756 - 3777
Abstract: Traversing terrain with good traction is crucial for achieving fast off-road navigation. Instead of manually designing costs based on terrain features, existing methods learn terrain properties directly from data via self-supervision to automatically penalize trajectories moving through undesirable terrain, but challenges remain in properly quantifying and mitigating the risk due to uncertainty in the learned models. To this end, we present evidential off-road autonomy (EVORA), a unified framework to learn uncertainty-aware traction model and plan risk-aware trajectories. For uncertainty quantification, we efficiently model both aleatoric and epistemic uncertainty by learning discrete traction distributions and probability densities of the traction predictor's latent features. Leveraging evidential deep learning, we parameterize Dirichlet distributions with the network outputs and propose a novel uncertainty-aware squared Earth Mover's Distance loss with a closed-form expression that improves learning accuracy and navigation performance. For risk-aware navigation, the proposed planner simulates state trajectories with the worst-case expected traction to handle aleatoric uncertainty and penalizes trajectories moving through terrain with high epistemic uncertainty. Our approach is extensively validated in simulation and on wheeled and quadruped robots, showing improved navigation performance compared to methods that assume no slip, assume the expected traction, or optimize for the worst-case expected cost.
PubDate: MON, 22 JUL 2024 09:15:56 -04
Issue No: Vol. 40, No. null (2024)
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- Regret-Based Sampling of Pareto Fronts for Multiobjective Robot Planning
Problems-
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Authors: Alexander Botros;Nils Wilde;Armin Sadeghi;Javier Alonso-Mora;Stephen L. Smith;
Pages: 3778 - 3794
Abstract: Many problems in robotics seek to simultaneously optimize several competing objectives. A conventional approach is to create a single cost function comprised of the weighted sum of the individual objectives. Solutions to this scalarized optimization problem are Pareto optimal solutions to the original multiobjective problem. However, finding an accurate representation of a Pareto front remains an important challenge. Uniformly spaced weights are often inefficient and do not provide error bounds. We address the problem of computing a finite set of weights whose optimal solutions closely approximate the solution of any other weight vector. To this end, we prove fundamental properties of the optimal cost as a function of the weight vector. We propose an algorithm that greedily adds the weight vector least-represented by the current set, and provide bounds on the regret. We extend our method to include suboptimal solvers for the scalarized optimization, and handle stochastic inputs to the planning problem. Finally, we illustrate that the proposed approach significantly outperforms baseline approaches for different robot planning problems with varying numbers of objective functions.
PubDate: TUE, 16 JUL 2024 09:16:15 -04
Issue No: Vol. 40, No. null (2024)
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- Fast Path Planning Through Large Collections of Safe Boxes
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Authors: Tobia Marcucci;Parth Nobel;Russ Tedrake;Stephen Boyd;
Pages: 3795 - 3811
Abstract: We present a fast algorithm for the design of smooth paths (or trajectories) that are constrained to lie in a collection of axis-aligned boxes. We consider the case where the number of these safe boxes is large, and basic preprocessing of them (such as finding their intersections) can be done offline. At runtime, we quickly generate a smooth path between given initial and terminal positions. Our algorithm designs trajectories that are guaranteed to be safe at all times, and detects infeasibility whenever such a trajectory does not exist. Our algorithm is based on two subproblems that we can solve very efficiently: finding a shortest path in a weighted graph, and solving (multiple) convex optimal-control problems. We demonstrate the proposed path planner on large-scale numerical examples, and we provide an efficient open-source software implementation, fastpathplanning.
PubDate: FRI, 26 JUL 2024 09:16:59 -04
Issue No: Vol. 40, No. null (2024)
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- Evetac: An Event-Based Optical Tactile Sensor for Robotic Manipulation
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Authors: Niklas Funk;Erik Helmut;Georgia Chalvatzaki;Roberto Calandra;Jan Peters;
Pages: 3812 - 3832
Abstract: Optical tactile sensors have recently become popular. They provide high spatial resolution, but struggle to offer fine temporal resolutions. To overcome this shortcoming, we study the idea of replacing the RGB camera with an event-based camera and introduce a new event-based optical tactile sensor called Evetac. Along with hardware design, we develop touch processing algorithms to process its measurements online at 1000 Hz. We devise an efficient algorithm to track the elastomer's deformation through the imprinted markers despite the sensor's sparse output. Benchmarking experiments demonstrate Evetac's capabilities of sensing vibrations up to 498 Hz, reconstructing shear forces, and significantly reducing data rates compared to RGB optical tactile sensors. Moreover, Evetac's output and the marker tracking provide meaningful features for learning data-driven slip detection and prediction models. The learned models form the basis for a robust and adaptive closed-loop grasp controller capable of handling a wide range of objects. We believe that fast and efficient event-based tactile sensors like Evetac will be essential for bringing human-like manipulation capabilities to robotics.
PubDate: MON, 15 JUL 2024 09:16:27 -04
Issue No: Vol. 40, No. null (2024)
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- Heterogeneous Policy Networks for Composite Robot Team Communication and
Coordination-
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Authors: Esmaeil Seraj;Rohan Paleja;Luis Pimentel;Kin Man Lee;Zheyuan Wang;Daniel Martin;Matthew Sklar;John Zhang;Zahi Kakish;Matthew Gombolay;
Pages: 3833 - 3849
Abstract: High-performing human–human teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the different roles of heterogeneous team members and adapt their communication protocols accordingly. Multiagent reinforcement learning (MARL) has attempted to develop computational methods for synthesizing such joint coordination–communication strategies, but emulating heterogeneous communication patterns across agents with different state, action, and observation spaces has remained a challenge. Without properly modeling agent heterogeneity, as in prior MARL work that leverages homogeneous graph networks, communication becomes less helpful and can even deteriorate the team's performance. In the past, we proposed heterogeneous policy networks (HetNet) to learn efficient and diverse communication models for coordinating cooperative heterogeneous teams. In this extended work, we extend HetNet to support scaling heterogeneous robot teams. Building on heterogeneous graph-attention networks, we show that HetNet not only facilitates learning heterogeneous collaborative policies, but also enables end-to-end training for learning highly efficient binarized messaging. Our empirical evaluation shows that HetNet sets a new state-of-the-art in learning coordination and communication strategies for heterogeneous multiagent teams by achieving an 5.84% to 707.65% performance improvement over the next-best baseline across multiple domains while simultaneously achieving a 200× reduction in the required communication bandwidth.
PubDate: MON, 22 JUL 2024 09:16:00 -04
Issue No: Vol. 40, No. null (2024)
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- Unlocking Human-Like Facial Expressions in Humanoid Robots: A Novel
Approach for Action Unit Driven Facial Expression Disentangled Synthesis-
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Authors: Xiaofeng Liu;Rongrong Ni;Biao Yang;Siyang Song;Angelo Cangelosi;
Pages: 3850 - 3865
Abstract: Humanoid robots often struggle to express the intricate and authentic facial expressions characteristic of humans, potentially hampering user engagement. To address this challenge, we introduce a comprehensive two-stage methodology to empower our autonomous affective robot with the capacity to exhibit rich and natural facial expressions. In the initial stage, we present an innovative action unit (AU) driven facial expression disentangled synthesis method, enabling the generation of nuanced robot facial expression images guided by AUs. By harnessing facial AUs within a framework of weakly supervised learning, we effectively surmount the scarcity of paired training data (comprising source and target facial expression images). To preserve the integrity of AUs while mitigating identity interference, we leverage a latent facial attribute space to disentangle expression-related and expression-unrelated cues, employing solely the former for expression synthesis. In the subsequent phase, we actualize an affective robot endowed with multifaceted degrees of freedom for facial movements, facilitating the embodiment of the synthesized fine-grained facial expressions. We devise a specialized motor command mapping network that serves as a conduit between the generated expression images and the robot's realistic facial responses. By utilizing the physical motor positions as constraints, we refine the prediction of precise motor commands from the robot's generated facial expressions. This refinement process ensures that the robot's facial movements authentically express accurate and natural expressions. Finally, qualitative and quantitative evaluations on the benchmarking Emotionet dataset verify the effectiveness of the proposed generation method. Results on the self-developed affective robot indicate that our method achieves a promising generation of specific facial expressions with given AUs, significantly enhancing the affective human–robot interaction.
PubDate: TUE, 02 JUL 2024 09:17:16 -04
Issue No: Vol. 40, No. null (2024)
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- DALI: Domain Adaptive LiDAR Object Detection via Distribution-Level and
Instance-Level Pseudolabel Denoising-
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Authors: Xiaohu Lu;Hayder Radha;
Pages: 3866 - 3878
Abstract: Object detection using LiDAR point clouds relies on a large amount of human-annotated samples when training the underlying detectors' deep neural networks. However, generating 3-D bounding box annotation for a large-scale dataset could be costly and time-consuming. Alternatively, unsupervised domain adaptation (UDA) enables a given object detector to operate on novel new data, with an unlabeled training dataset, by transferring the knowledge learned from training labeled source domain data to the new unlabeled target domain. Pseudolabel strategies, which involve training the 3-D object detector using target-domain predicted bounding boxes from a pretrained model, are commonly used in UDA. However, these pseudolabels often introduce noise, impacting performance. In this article, we introduce the domain adaptive LiDAR (DALI) object detection framework to address noise at both distribution and instance levels. First, a posttraining size normalization (PTSN) strategy is developed to mitigate bias in pseudolabel size distribution by identifying an unbiased scale after network training. To address instance-level noise between pseudolabels and corresponding point clouds, two pseudopoint clouds generation (PPCG) strategies, ray-constrained and constraint-free, are developed to generate pseudopoint clouds for each instance, ensuring the consistency between pseudolabels and pseudopoints during training. We demonstrate the effectiveness of our method on the publicly available and popular datasets KITTI, Waymo, and nuScenes. We show that the proposed DALI framework achieves state-of-the-art results and outperforms leading approaches on most of the domain adaptation tasks.
PubDate: TUE, 30 JUL 2024 09:16:56 -04
Issue No: Vol. 40, No. null (2024)
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- Consensus Complementarity Control for Multicontact MPC
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Authors: Alp Aydinoglu;Adam Wei;Wei-Cheng Huang;Michael Posa;
Pages: 3879 - 3896
Abstract: We propose a hybrid model predictive control algorithm, consensus complementarity control, for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks, which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are too computationally complex to run at real-time rates. We present a method based on the alternating direction method of multipliers that is capable of high-speed reasoning over potential contact events. Via a consensus formulation, our approach enables parallelization of the contact scheduling problem. We validate our results on five numerical examples, including four high-dimensional frictional contact problems, and a physical experimentation on an underactuated multicontact system. We further demonstrate the effectiveness of our method on a physical experiment accomplishing a high-dimensional, multicontact manipulation task with a robot arm.
PubDate: TUE, 30 JUL 2024 09:16:56 -04
Issue No: Vol. 40, No. null (2024)
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