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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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- IEEE Transactions on Robotics Publication Information
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Pages: C2 - C2
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- IEEE Transactions on Robotics Information for Authors
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Pages: C3 - C3
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Blank Page
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Pages: C4 - C4
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Human to Robot Hand Motion Mapping Methods: Review and Classification
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Authors: Roberto Meattini;Raúl Suárez;Gianluca Palli;Claudio Melchiorri;
Pages: 842 - 861
Abstract: In this article, the variety of approaches proposed in the literature to address the problem of mapping human to robot hand motions are summarized and discussed. We particularly attempt to organize under macrocategories the great quantity of presented methods that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology, and declared goals of the mappings. First, a brief historical overview is reported, in order to provide a look on the emergence of the human to robot hand mapping problem as a both conceptual and analytical challenge that is still open nowadays. Thereafter, the survey mainly focuses on a classification of modern mapping methods under the following six categories: direct joint, direct Cartesian, task-oriented, dimensionality reduction based, pose recognition based, and hybrid mappings. For each of these categories, the general view that associates the related reported studies is provided, and representative references are highlighted. Finally, a concluding discussion along with the authors' point of view regarding future desirable trends are reported.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- A Decade of MRI Compatible Robots: Systematic Review
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Authors: Muhammad Umar Farooq;Seong Young Ko;
Pages: 862 - 884
Abstract: Magnetic resonance imaging (MRI) offers better visualization for diagnosis, interventional radiology, and surgery from other imaging modalities without X-ray exposure. Medical robots have provided solutions for many surgical and rehabilitation procedures. A symbiosis of MRI and robotics can allow manipulation in closed MR-gantry, decrease trauma, operation, and recovery time for patients and improve the dexterity, convenience, and surgical outcome for physicians. However, the inherent properties and limitations of MR scanners had hindered their development. Technological advancements in the field of computers, additive manufacturing, and sensing in the past decade have changed robotics and the focus shifted again toward MRI-compatible robots. This article provides a compendium of the state-of-the-art literature on robotic systems developed for the MRI environment presented between 2009 and 2021. The systematic review discusses surgical and fMRI study robots, the actuation, and sensing solutions developed to assist such robots, recent research emphasis, current limitations, and prospects.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- ViTAL: Vision-Based Terrain-Aware Locomotion for Legged Robots
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Authors: Shamel Fahmi;Victor Barasuol;Domingo Esteban;Octavio Villarreal;Claudio Semini;
Pages: 885 - 904
Abstract: This article focuses on vision-based planning strategies for legged robots that separate locomotion planning into foothold selection and pose adaptation. Current pose adaptation strategies optimize the robot's body pose relative to given footholds. If these footholds are not reached, the robot may end up in a state with no reachable safe footholds. Therefore, we present a vision-based terrain-aware locomotion (ViTAL) strategy that consists of novel pose adaptation and foothold selection algorithms. ViTAL introduces a different paradigm in pose adaptation that does not optimize the body pose relative to given footholds, but the body pose that maximizes the chances of the legs in reaching safe footholds. ViTAL plans footholds and poses based on skills that characterize the robot's capabilities and its terrain awareness. We use the 90-kg HyQ and 140-kg HyQReal quadruped robots to validate ViTAL and show that they are able to climb various obstacles, including stairs, gaps, and rough terrains, at different speeds and gaits. We compare ViTAL with a baseline strategy that selects the robot pose based on given selected footholds and show that ViTAL outperforms the baseline.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body
Motion Planning-
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Authors: Avadesh Meduri;Paarth Shah;Julian Viereck;Majid Khadiv;Ioannis Havoutis;Ludovic Righetti;
Pages: 905 - 922
Abstract: Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear model predictive control (MPC) framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes, and transitioning online between different gaits. Furthermore, the ability of BiConMP to generate nontrivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- An Investigation on the Effect of Actuation Pattern on the Power
Consumption of Legged Robots for Extraterrestrial Exploration-
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Authors: Yuan Hu;Weizhong Guo;Rongfu Lin;
Pages: 923 - 940
Abstract: Legged robots have great potential to be extraterrestrial exploration rovers of extraordinary versatility. Minimizing power consumption is of vital importance in the scenarios of extraterrestrial explorations. The actuation pattern, which refers to the combination of necessary actuators that output torque, has a significant influence on the power consumption of legged robots. This article seeks to investigate the effect of actuation patterns on the power consumption of legged robots that perform motion in a quasi-static manner. The power consumption model of legged robots considering actuation patterns is deduced. Based on that, the effect of the actuation pattern on mechanical power and heat power, which are the main power-loss terms, is investigated. The lowest power consumption under various conditions achieved by different actuation patterns is investigated. Simulation results show that the power consumption can be reduced by choosing the actuation pattern properly. Furthermore, the principles of selecting the optimal actuation pattern from the perspective of power consumption are summarized, which are expected to facilitate the minimal power consumption motion planning of legged robots.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- $mathbf{alpha+}$
-WaLTR:+Adaptive+Wheel-and-Leg+Transformable+Robot+for+Versatile+Multiterrain+Locomotion&rft.title=IEEE+Transactions+on+Robotics&rft.issn=1546-1904&rft.date=2023&rft.volume=39&rft.spage=941&rft.epage=958&rft.aulast=Lee;&rft.aufirst=Chuanqi&rft.au=Chuanqi+Zheng;Siddharth+Sane;Kangneoung+Lee;Vishnu+Kalyanram;Kiju+Lee;">$mathbf{alpha }$
-WaLTR: Adaptive Wheel-and-Leg Transformable Robot for
Versatile Multiterrain Locomotion-
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Authors: Chuanqi Zheng;Siddharth Sane;Kangneoung Lee;Vishnu Kalyanram;Kiju Lee;
Pages: 941 - 958
Abstract: Adaptability is a fundamental yet challenging requirement for mobile robot locomotion. This article presents $alpha$-WaLTR, a new adaptive wheel-and-leg transformable robot for versatile multiterrain mobility. The robot has four passively transformable wheels, where each wheel consists of a central gear and multiple leg segments with embedded spring suspension for vibration reduction. These wheels enable the robot to traverse various terrains, obstacles, and stairs while retaining the simplicity in primary control and operation principles of conventional wheeled robots. The chassis dimensions and the center of gravity location were determined by a multiobjective design optimization process aimed at minimizing the weight and maximizing the robot's pitch angle for obstacle climbing. Unity-based simulations guided the selection of the design variables associated with the transformable wheels. Following the design process, $alpha$-WaLTR with an embedded sensing and control system was developed. Experiments showed that the spring suspension on the wheels effectively reduced the vibrations when operated in the legged mode and verified that the robot's versatile locomotion capabilities were highly consistent with the simulations. The system-level integration with an embedded control system was demonstrated via autonomous stair detection, navigation, and climbing capabilities.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Mechanical Designs for Field Undulatory Locomotion by a Wheeled Snake-Like
Robot With Decoupled Neural Oscillators-
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Authors: Yasuhiro Fukuoka;Kotaro Otaka;Ryo Takeuchi;Kaito Shigemori;Kousuke Inoue;
Pages: 959 - 977
Abstract: In this article, we demonstrate the value of using decoupled neural oscillators in conjunction with appropriate mechanical designs for snake-like robots engaging in field locomotion. Even though our typical wheeled snake robot with decoupled neural oscillators incorporating angle joint feedback can generate consistent lateral undulation, it lacks robustness. A purely decoupled actuation strategy is not practical for field locomotion. However, the application of one-way wheels and elasticity between the modules improves robustness, allowing our robot to traverse outdoor open terrain. On the other hand, the physical form of the undulatory motion must be varied to pass through intricate narrow-field environments. The lack of neural constraints between the modules passively enables this variability and also facilitates each module independently gaining propulsion (i.e., by means of reflexive thrust via sensory feedback to the neural oscillator and one-way traction generated by one-way side rollers). The designed robot is able to engage in fast creeping motion through an environment containing randomly distributed logs. The results of the field experiments show that the specially designed mechanisms in conjunction with decoupled oscillators can enhance robustness, variability, and independent local propulsion.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- KDF: Kinodynamic Motion Planning via Geometric Sampling-Based Algorithms
and Funnel Control-
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Authors: Christos K. Verginis;Dimos V. Dimarogonas;Lydia E. Kavraki;
Pages: 978 - 997
Abstract: We integrate sampling-based planning techniques with funnel-based feedback control to develop KDF, a new framework for solving the kinodynamic motion-planning problem via funnel control. The considered systems evolve subject to complex, nonlinear, and uncertain dynamics (also known as differential constraints). First, we use a geometric planner to obtain a high-level safe path in a user-defined extended free space. Second, we develop a low-level funnel control algorithm that guarantees safe tracking of the path by the system. Neither the planner nor the control algorithm uses information on the underlying dynamics of the system, which makes the proposed scheme easily distributable to a large variety of different systems and scenarios. Intuitively, the funnel control module is able to implicitly accommodate the dynamics of the system, allowing hence the deployment of purely geometrical motion planners. Extensive computer simulations and hardware experiments with a 6-DOF robotic arm validate the proposed approach.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Closing the Planning–Learning Loop With Application to Autonomous
Driving-
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Authors: Panpan Cai;David Hsu;
Pages: 998 - 1011
Abstract: Real-time planning under uncertainty is critical for robots operating in complex dynamic environments. Consider, for example, an autonomous robot vehicle driving in dense, unregulated urban traffic of cars, motorcycles, buses, etc. The robot vehicle has to plan in both short and long terms, in order to interact with many traffic participants of uncertain intentions and drive effectively. Planning explicitly over a long time horizon, however, incurs prohibitive computational cost and is impractical under real-time constraints. To achieve real-time performance for large-scale planning, this work introduces a new algorithm Learning from Tree Search for Driving (LeTS-Drive), which integrates planning and learning in a closed loop, and applies it to autonomous driving in crowded urban traffic in simulation. Specifically, LeTS-Drive learns a policy and its value function from data provided by an online planner, which searches a sparsely sampled belief tree; the online planner in turn uses the learned policy and value functions as heuristics to scale up its run-time performance for real-time robot control. These two steps are repeated to form a closed loop so that the planner and the learner inform each other and improve in synchrony. The algorithm learns on its own in a self-supervised manner, without human effort on explicit data labeling. Experimental results demonstrate that LeTS-Drive outperforms either planning or learning alone, as well as open-loop integration of planning and learning.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Knowledge Database-Based Multiobjective Trajectory Planning of 7-DOF
Manipulator With Rapid and Continuous Response to Uncertain Fast-Flying
Objects-
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Authors: Ziwu Ren;Biao Hu;Zhicheng Wang;Lining Sun;Qiuguo Zhu;
Pages: 1012 - 1028
Abstract: The problems of a 7-degree of freedom (DOF) manipulator with rapid and continuous response to uncertain fast-flying objects are addressed: 1) how to effectively solve trajectory planning of the 7-DOF manipulator with multiple criteria; and 2) how to make the 7-DOF manipulator realize the rapid and continuous response to uncertain fast-flying objects. In the proposed approach, based on the trajectory parameterization of the 7-DOF manipulator, a multiobjective teaching-learning-based optimization (MOTLBO) algorithm is adopted to find a close representation of the Pareto optimal set rather than a single solution. As such, an optimal solution can be chosen as digital knowledge information. A new methodology based on a knowledge base representing and learning the operation environment, that is, skill digitization, is presented, which enables the 7-DOF manipulator to realize the rapid and continuous response skill. Simulation and practical testing results of a ping-pong robot validate the feasibility and effectiveness of the proposed approach, in which the online trajectory generation spends only around 1 ms.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Online Search-Based Collision-Inclusive Motion Planning and Control for
Impact-Resilient Mobile Robots-
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Authors: Zhouyu Lu;Zhichao Liu;Merrick Campbell;Konstantinos Karydis;
Pages: 1029 - 1049
Abstract: This article focuses on the emerging paradigm shift of collision-inclusive motion planning and control for impact-resilient mobile robots, and develops a unified hierarchical framework for navigation in unknown and partially observable cluttered spaces. At the lower level, we develop a deformation recovery control and trajectory replanning strategy that handles collisions that may occur at run time, locally. The low-level system actively detects collisions (via embedded Hall effect sensors on a mobile robot built in-house), enables the robot to recover from them, and locally adjusts the postimpact trajectory. Then, at the higher level, we propose a search-based planning algorithm to determine how to best utilize potential collisions to improve certain metrics, such as control energy and computational time. Our method builds upon A* with jump points. We generate a novel heuristic function, and a collision checking and adjustment technique, thus making the A* algorithm converge faster to reach the goal by exploiting and utilizing possible collisions. The overall hierarchical framework generated by combining the global A* algorithm and the local deformation recovery and replanning strategy, as well as individual components of this framework, are tested extensively both in simulation and experimentally. An ablation study draws links to related state-of-the-art search-based collision-avoidance planners (for the overall framework), as well as search-based collision-avoidance and sampling-based collision-inclusive global planners (for the higher level). Results demonstrate our method's efficacy for collision-inclusive motion planning and control in unknown environments with isolated obstacles for a class of impact-resilient robots operating in 2-D.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Active Inference and Behavior Trees for Reactive Action Planning and
Execution in Robotics-
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Authors: Corrado Pezzato;Carlos Hernández Corbato;Stefan Bonhof;Martijn Wisse;
Pages: 1050 - 1069
Abstract: In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, we specify the nominal behavior offline, through BTs. However, in contrast to previous approaches, we introduce a new type of leaf node to specify the desired state to be achieved rather than an action to execute. The decision of which action to execute to reach the desired state is performed online through active inference. This results in continual online planning and hierarchical deliberation. By doing so, an agent can follow a predefined offline plan while still keeping the ability to locally adapt and take autonomous decisions at runtime, respecting safety constraints. We provide proof of convergence and robustness analysis, and we validate our method in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment. The results showed improved runtime adaptability with a fraction of the hand-coded nodes compared to classical BTs.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Bounds on Optimal Revisit Times in Persistent Monitoring Missions With a
Distinct and Remote Service Station-
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Authors: Sai Krishna Kanth Hari;Sivakumar Rathinam;Swaroop Darbha;Satyanarayana Gupta Manyam;Krishna Kalyanam;David Casbeer;
Pages: 1070 - 1086
Abstract: Persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. Unmannned aerial vehicles (UAVs) can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this article, we consider the case in which the service station is not colocated with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Robust Task Scheduling for Heterogeneous Robot Teams Under Capability
Uncertainty-
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Authors: Bo Fu;William Smith;Denise M. Rizzo;Matthew Castanier;Maani Ghaffari;Kira Barton;
Pages: 1087 - 1105
Abstract: This article develops a stochastic programming framework for multiagent systems, where task decomposition, assignment, and scheduling problems are simultaneously optimized. The framework can be applied to heterogeneous mobile robot teams with distributed subtasks. Examples include pandemic robotic service coordination, explore and rescue, and delivery systems with heterogeneous vehicles. Owing to their inherent flexibility and robustness, multiagent systems are applied in a growing range of real-world problems that involve heterogeneous tasks and uncertain information. Most previous works assume one fixed way to decompose a task into roles that can later be assigned to the agents. This assumption is not valid for a complex task where the roles can vary and multiple decomposition structures exist. Meanwhile, it is unclear how uncertainties in task requirements and agent capabilities can be systematically quantified and optimized under a multiagent system setting. A representation for complex tasks is proposed: agent capabilities are represented as a vector of random distributions, and task requirements are verified by a generalizable binary function. The conditional value at risk is chosen as a metric in the objective function to generate robust plans. An efficient algorithm is described to solve the model, and the whole framework is evaluated in two different practical test cases: capture-the-flag and robotic service coordination during a pandemic (e.g., COVID-19). Results demonstrate that the framework is generalizable, is scalable up to 140 agents and 40 tasks for the example test cases, and provides low-cost plans that ensure a high probability of success.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Multi-Robot Pickup and Delivery via Distributed Resource Allocation
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Authors: Andrea Camisa;Andrea Testa;Giuseppe Notarstefano;
Pages: 1106 - 1118
Abstract: In this article, we consider a large-scale instance of the classical pickup-and-delivery vehicle routing problem that must be solved by a network of mobile cooperating robots. Robots must self-coordinate and self-allocate a set of pickup/delivery tasks while minimizing a given cost figure. This results in a large, challenging mixed-integer linear problem that must be cooperatively solved without a central coordinator. We propose a distributed algorithm based on a primal decomposition approach that provides a feasible solution to the problem in finite time. An interesting feature of the proposed scheme is that each robot computes only its own block of solution, thereby preserving privacy of sensible information. The algorithm also exhibits attractive scalability properties that guarantee solvability of the problem even in large networks. To the best of our knowledge, this is the first attempt to provide a scalable distributed solution to the problem. The algorithm is first tested through Gazebo simulations on a ROS 2 platform, highlighting the effectiveness of the proposed solution. Finally, experiments on a real testbed with a team of ground and aerial robots are provided.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Guiding Vector Fields for the Distributed Motion Coordination of Mobile
Robots-
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Authors: Weijia Yao;Héctor García de Marina;Zhiyong Sun;Ming Cao;
Pages: 1119 - 1135
Abstract: In this article, we propose coordinating guiding vector fields to achieve two tasks simultaneously with a team of robots: first, the guidance and navigation of multiple robots to possibly different paths or surfaces typically embedded in 2-D or 3-D, and second, their motion coordination while tracking their prescribed paths or surfaces. The motion coordination is defined by desired parametric displacements between robots on the path or surface. Such a desired displacement is achieved by controlling the virtual coordinates, which correspond to the path or surface's parameters, between guiding vector fields. Rigorous mathematical guarantees underpinned by dynamical systems theory and Lyapunov theory are provided for the effective distributed motion coordination and navigation of robots on paths or surfaces from all initial positions. As an example for practical robotic applications, we derive a control algorithm from the proposed coordinating guiding vector fields for a Dubins-car-like model with actuation saturation. Our proposed algorithm is distributed and scalable to an arbitrary number of robots. Furthermore, extensive illustrative simulations and fixed-wing aircraft outdoor experiments validate the effectiveness and robustness of our algorithm.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Exact and Approximate Heterogeneous Bayesian Decentralized Data Fusion
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Authors: Ofer Dagan;Nisar R. Ahmed;
Pages: 1136 - 1150
Abstract: In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally by autonomous agents are frequently assumed to be over the same set of variables (homogeneous). This requires each agent to process and communicate the full global joint distribution, and thus, leads to high computation and communication costs irrespective of relevancy to specific local objectives. This work formulates and studies heterogeneous decentralized fusion problems, defined as the set of problems in which either the communicated or the processed distributions describe different, but overlapping, random states of interest that are subsets of a larger full global joint state. We exploit the conditional independence structure of such problems and provide a rigorous derivation of novel exact and approximate conditionally factorized heterogeneous fusion rules. We further develop a new version of the homogeneous channel filter algorithm to enable conservative heterogeneous fusion for smoothing and filtering scenarios in dynamic problems. Numerical examples show more than 99.5% potential communication reduction for heterogeneous channel filter fusion, and a multitarget tracking simulation shows that these methods provide consistent estimates while remaining computationally scalable.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Grasping Living Objects With Adversarial Behaviors Using Inverse
Reinforcement Learning-
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Authors: Zhe Hu;Yu Zheng;Jia Pan;
Pages: 1151 - 1163
Abstract: Living objects are difficult to grasp since they can actively elude capture by adopting adversarial behaviors that are extremely hard to model or predict. In this case, an inappropriately strong contact force may hurt the struggling living objects and a grasping algorithm that can minimize the contact force whenever possible is required. To solve this challenging task, in this article, we present a reinforcement-learning (RL)-based algorithm with two stages: the pregrasp stage and the in-hand stage. In the pregrasp stage, the robot focuses on the living object's adversarial behavior and approaches it in a reliable manner. In particular, we use inverse RL to encode the living object's adversarial behavior into a reward function. The negative value of the learned reward function is then used to train a high-quality grasping policy that can compete with the living object's adversarial behavior with the RL framework. In the in-hand stage, we use RL to train a grasp policy such that the dexterous hand can grab the living object with the minimal force. A set of dense rewards are also specifically designed to encourage the robot to grasp and hold the living object persistently. To further improve the grasp performance, we explicitly take into account the structure of the dexterous robot hand by treating the hand as a graph and adopting graph convolutional network to formulate the grasping policy. We conduct a set of experiments to demonstrate the performance of our proposed method, in which the robot can grasp living objects with the success rate of 90% and 95% in the pregrasp and in-hand stages, respectively. The contact force applied by the robotic hand to the living object is dramatically reduced in comparison with the baseline grasping policy.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- A Novel Scaffold-Reinforced Actuator With Tunable Attitude Ability for
Grasping-
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Authors: Pei Jiang;Ji Luo;Jiaxing Li;Michael Z. Q. Chen;Yonghua Chen;Yang Yang;Rui Chen;
Pages: 1164 - 1177
Abstract: Owing to high compliance, adaptiveness, and easy controllability, soft actuators are widely adopted in soft grippers to grasp irregularly shaped or fragile objects. The specific motions can be preprogrammed into the flexible and constrained structures of the actuator, which provides an inexpensive and convenient method for desired motions. However, most preprogrammed structures cannot change the constraints on the actuator to achieve different kinds of deformations, which limits the motion diversities of actuators. This article proposes a scaffold reinforcement mechanism, where rotatable scaffolds distribute on the surface of the soft structure. The orientation adjustments of the scaffolds can change the deformation constraint of the actuator, which results in different kinds of motions. Based on the scaffold reinforcement mechanism, a scaffold-reinforced actuator is proposed, which can achieve bending motion and complex helical motion in the 3-D space by properly adjusting the orientation of the scaffolds. In addition, both the kinematic and mechanical models are proposed to forecast the behavior of the actuator when driven by cable displacement or tension force. Experimental results verify the validity of the theoretical model, and the actuator can achieve an independent control of bending and helical motion, which can be adopted in applications where both high dexterity and flexibility are required.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Anthropomorphic Twisted String-Actuated Soft Robotic Gripper With
Tendon-Based Stiffening-
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Authors: Revanth Konda;David Bombara;Steven Swanbeck;Jun Zhang;
Pages: 1178 - 1195
Abstract: Realizing high-performance soft robotic grippers is challenging because of the inherent limitations of the soft actuators and artificial muscles that drive them, including low force generation, small actuation range, and poor compactness to name a few. Despite advances in this area, realizing compact soft grippers, which exhibit high dexterity and force output, is still challenging. This article explores using twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been widely used in numerous robotic applications, but their inclusion in soft robots has been limited. The proposed design of the gripper was inspired by the human hand, with four fingers and a thumb. Tunable stiffness was implemented in the fingers by using antagonistic TSAs. The fingers' bending angles, actuation speed, blocked force output, and stiffness tuning are experimentally characterized. The gripper achieves a score of 6 on the Kapandji test and recreate 31 of the 33 grasps of the Feix GRASP taxonomy. It exhibits a maximum grasping force of 72 N, which is almost 13 times its own weight. A comparison study reveals that the proposed gripper exhibits equivalent or superior performance compared to other similar soft grippers.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- $AX+=+YB$
&rft.title=IEEE+Transactions+on+Robotics&rft.issn=1546-1904&rft.date=2023&rft.volume=39&rft.spage=1196&rft.epage=1211&rft.aulast=Ha;&rft.aufirst=Junhyoung&rft.au=Junhyoung+Ha;">Probabilistic Framework for Hand–Eye and Robot–World Calibration $AX =
YB$-
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Authors: Junhyoung Ha;
Pages: 1196 - 1211
Abstract: Hand–eye and robot–world calibration is a problem in which the unknown homogeneous transformations $X$ and $Y$ must be estimated for a loop closure equation $AX = YB$ for a set of transformation measurement pairs $lbrace (A_{i}, B_{i}) rbrace$. Previous studies on $AX=YB$ have mainly relied on linear least-squares minimization followed by nonlinear iterative optimization for solution refinement to minimize the distances between $A_{i} X$ and $Y B_{i}$. However, these methods have not been fully clarified, particularly in terms of calibration dependence on the coordination of $A,B,X$, and $Y$ along the system loop, as well as the underlying noise distributions of $A_{i}$ and $B_{i}$. They also lack flexibility in the noise properties of individual measurements; thus, they cannot incorporate the relative reliability between measurements. To address these limitations, we propose a probabilistic framework for hand–eye and robot–world calibration. The proposed framework clarifies the unclear aspects of existing methods by revealing their underlying assumptions regarding system noise. Consequently, it identifies the -pplicability of distance minimization to a given calibration problem and provides the optimal coordination of transformations for distance minimization. For cases in which distance minimization is inapplicable, an iterative algorithm for the maximum likelihood estimation is proposed, whereby the different noise properties of individual measurements can be accounted for. An estimation uncertainty analysis is presented for the proposed iterative algorithm to quantify the expected estimation accuracy. The presented theories and the proposed algorithm are validated using a set of numerical and hardware experiments. The code for the iterative algorithm and the estimation uncertainty is available at https://github.com/hjhdog1/probabilisticAXYB.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Multimodal Learning of Keypoint Predictive Models for Visual Object
Manipulation-
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Authors: Sarah Bechtle;Neha Das;Franziska Meier;
Pages: 1212 - 1224
Abstract: Humans have impressive generalization capabilities when it comes to manipulating objects and tools in completely novel environments. These capabilities are, at least partially, a result of humans having internal models of their bodies and any grasped object. How to learn such body representations for robots remains an open problem. In this work, we present a self-supervised learning approach that extends a robot's kinematic model for object manipulation from visual latent representations. Our framework comprises two components: First, we present our multimodal keypoint detector: A neural network autoencoder architecture that fuses proprioception and vision during learning to predict visual key points on an object; second, we show how we can learn an extension of the kinematic chain of the robot by regressing virtual joints from the visual keypoints predicted by our multimodal keypoint detector. Our evaluation shows that our approach learns to consistently predict visual keypoints on objects in the manipulator's hand and, thus, can easily facilitate learning an extended kinematic chain to include the object grasped in various configurations, from a few seconds of visual data. Finally, we show that this extended kinematic chain lends itself for object manipulation tasks such as placing a grasped object and present experiments in simulation and on hardware.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Object Detection Using Sim2Real Domain Randomization for Robotic
Applications-
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Authors: Dániel Horváth;Gábor Erdős;Zoltán Istenes;Tomáš Horváth;Sándor Földi;
Pages: 1225 - 1243
Abstract: Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated. Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. With the proposed domain randomization method, we could shrink the reality gap to a satisfactory level, achieving 86.32% and 97.38% $mathrm{{mAP}}_{50}$ scores, respectively, in the case of zero-shot and one-shot transfers, on our manually annotated dataset containing 190 real images. Our solution fits for industrial use as the data generation process takes less than 0.5 s per image and the training lasts only around 12 h, on a GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar classes of objects by having access to only one real image for training. To our best knowledge, this is the only work thus far satisfying these constraints.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Deep Learning Reactive Robotic Grasping With a Versatile Vacuum Gripper
-
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Authors: Hui Zhang;Jef Peeters;Eric Demeester;Karel Kellens;
Pages: 1244 - 1259
Abstract: In this article, a six-step approach is proposed to simulate the grasp and evaluate the grasp quality for a versatile vacuum gripper by tracking the deformation and force-torque wrench of the gripping pad. Over 100 K synthetic grasps are generated for neural network training. Furthermore, a gripping attention convolutional neural network (GA-CNN) is developed to predict the grasp quality for real-world grasp, running by 15 Hz closed-loop control with the real-time robotic observation and force-torque feedback. Various experiments in both the simulation and physical grasps indicate that our GA-CNN can focus on the crucial region of the soft gripping pad to predict grasp qualities and perform a lower average error compared with a same-scale traditional CNN. In addition, the complexity of grasping clutters is defined from Level 1 to Level 9. The proposed grasping method achieves an average success rate of 90.2% for static clutters at Level 1 to Level 8 and an average success rate of >80.0% for dynamic grasping at Level 1 to Level 7, which outperforms state-of-the-art grasping methods.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Kinegami: Algorithmic Design of Compliant Kinematic Chains From Tubular
Origami-
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Authors: Wei-Hsi Chen;Woohyeok Yang;Lucien Peach;Daniel E. Koditschek;Cynthia R. Sung;
Pages: 1260 - 1280
Abstract: Origami processes can generate both rigid and compliant structures from the same homogeneous sheet material. In this article, we advance the origami robotics literature by showing that it is possible to construct an arbitrary rigid kinematic chain with prescribed joint compliance from a single tubular sheet. Our “Kinegami” algorithm converts a Denavit–Hartenberg specification into a single-sheet crease pattern for an equivalent serial robot mechanism by composing origami modules from a catalogue. The algorithm arises from the key observation that tubular origami linkage design reduces to a Dubins path planning problem. The automatically generated structural connections and movable joints that realize the specified design can also be endowed with independent user-specified compliance. We apply the Kinegami algorithm to a number of common robot mechanisms and hand-fold their algorithmically generated single-sheet crease patterns into functioning kinematic chains. We believe this is the first completely automated end-to-end system for converting an abstract manipulator specification into a physically realizable origami design that requires no additional human input.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Choosing Stiffness and Damping for Optimal Impedance Planning
-
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Authors: Mathew Jose Pollayil;Franco Angelini;Guiyang Xin;Michael Mistry;Sethu Vijayakumar;Antonio Bicchi;Manolo Garabini;
Pages: 1281 - 1300
Abstract: The attention given to impedance control in recent years does not match a similar focus on the choice of impedance values that the controller should execute. Current methods are hardly general and often compute fixed controller gains relying on the use of expensive sensors. In this article, we address the problem of online impedance planning for Cartesian impedance controllers that do not assign the closed-loop inertia. We propose an optimization-based algorithm that, given the Cartesian inertia, computes the stiffness and damping gains without relying on force/torque measurements and so that the effects of perturbations are less than a maximum acceptable value. By doing so, we increase robot resilience to unexpected external disturbances while guaranteeing performance and robustness. The algorithm provides an analytical solution in the case of impedance-controlled robots with diagonally dominant inertia matrix. Instead, established numerical methods are employed to deal with the more common case of nondiagonally dominant inertia. Our work attempts to create a general impedance planning framework, which needs no additional hardware and is easily applicable to any robotic system. Through experiments on real robots, including a quadruped and a robotic arm, our method is shown to be employable in real time and to lead to satisfactory behaviors.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- An Unconstrained Convex Formulation of Compliant Contact
-
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Authors: Alejandro M. Castro;Frank N. Permenter;Xuchen Han;
Pages: 1301 - 1320
Abstract: In this article, we present a convex formulation of compliant frictional contact and a robust performant method to solve it in practice. By analytically eliminating contact constraints, we obtain an unconstrained convex problem. Our solver has proven global convergence and warm-starts effectively, enabling simulation at interactive rates. We develop compact analytical expressions of contact forces allowing us to describe our model in clear physical terms and to rigorously characterize our approximations. Moreover, this enables us not only to model point contact but also to incorporate sophisticated models of compliant contact patches. Our time stepping scheme includes the midpoint rule, which we demonstrate achieves second order accuracy even with frictional contact. We introduce a number of accuracy metrics and show that our method outperforms the existing commercial and open-source alternatives without sacrificing accuracy. Finally, we demonstrate the robust simulation of robotic manipulation tasks at interactive rates, with accurately resolved stiction and contact transitions, as required for meaningful sim-to-real transfer. Our method is implemented in the open-source robotics toolkit Drake.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Interaction Control of a Robotic Manipulator With the Surface of
Deformable Object-
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Authors: Athanasios C. Dometios;Costas S. Tzafestas;
Pages: 1321 - 1340
Abstract: Robotic manipulation of deformable objects has drawn the attention of researchers over the past few years and is associated with a large spectrum of new application perspectives. In this article, we present an efficient integrated motion planning framework to effectively and accurately control a robotic manipulator executing interactive tasks on the surface of a deformable object. The proposed interactive motion planning framework is based on a mesh representation of the object, integrating three efficient preprocessing algorithmic steps, including visual object segmentation, finite element method deformation tracking, and local mesh parameterization. The use of barycentric coordinates, defined on the mesh triangles, enables the establishment of bijective transformations between the deformable part of an object surface and its planar (static and dynamic) parameterized mapping. By merging these spatial transformations with the preprocessing steps, in combination with an active stiffness scheme for robot manipulator control, we are able to achieve accurate and reactive motion planning of interactive trajectories, even under large and persistent visual occlusions (such as due to the presence of the robot in the visual scene). An extensive experimental evaluation study is presented, involving a robotic manipulator in interaction with a hemispherical model of controllable periodic active deformation, which permits precise ground truth derivation. Motion planning accuracy is evaluated in comparison with our previous direct vision-based approach, showing clearly superior performance of the proposed approach under all experimental conditions. The performance of the proposed framework is also further highlighted in tasks involving physical point tracking, interactive programming by human demonstration, as well as contact force regulation.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- MIRRAX: A Reconfigurable Robot for Limited Access Environments
-
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Authors: Wei Cheah;Keir Groves;Horatio Martin;Harriet Peel;Simon Watson;Ognjen Marjanovic;Barry Lennox;
Pages: 1341 - 1352
Abstract: The development of mobile robot platforms for inspection has gained traction in recent years. However, conventional mobile robots are unable to address the challenge of operating in extreme environments where the robot is required to traverse narrow gaps in highly cluttered areas with restricted access, typically through narrow ports. This article presents MIRRAX, a robot designed to meet these challenges by way of its reconfigurable capability. Controllers for the robot are detailed, along with an analysis on the controllability of the robot given the use of mecanum wheels in a variable configuration. Characterization on the robot's performance identified suitable configurations for operating in narrow environments. The experimental validation of the robot's controllability shows good agreement with the theoretical analysis and the capability to address the challenges of accessing entry ports as small as 150-mm diameter, as well as navigating through cluttered environments. This article also presents results from a deployment in a Magnox facility at the Sellafield nuclear site in the U.K.—the first robot to ever do so, for remote inspection and mapping.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Auto-Optimizing Connection Planning Method for Chain-Type Modular
Self-Reconfiguration Robots-
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Authors: Haobo Luo;Tin Lun Lam;
Pages: 1353 - 1372
Abstract: Chain-type modular robots are capable of self-reconfiguration (SR), where the connection relationship between modules is changed according to the environment and tasks. This article focuses on the connection planning of SR based on multiple in-degree single out-degree (MISO) modules. The goal is to calculate the optimal connection planning solution: the sequence with the fewest detachment and attachment actions. To this end, we propose an auto-optimizing connection planning method that contains a polynomial-time algorithm to calculate near-optimal solutions and an exponential-time algorithm to further optimize the solutions automatically when some CPUs are idle. The method combines rapidity and optimality in the face of an NP-complete problem by using configuration pointers, strings that uniquely specify the robot's configuration. Our polynomial-time algorithm, in-degree matching (IM) uses the interchangeability of connection points to reduce reconfiguration steps. Our exponential-time algorithm, tree-based branch and bound (TBB) further optimizes the solutions to the optimum by a new branching strategy and stage cost. In the experiments, we verify the feasibility of the auto-optimizing method combining IM and TBB, and demonstrate the superiority of IM over Greedy-CM in the SR of MISO modules and the near-optimality of IM compared to the optimal solutions of TBB.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Simultaneous Online Registration-Independent Stiffness Identification and
Tip Localization of Surgical Instruments in Robot-Assisted Eye Surgery-
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Authors: Ali Ebrahimi;Shahriar Sefati;Peter Gehlbach;Russell H. Taylor;Iulian I. Iordachita;
Pages: 1373 - 1387
Abstract: Notable challenges during retinal surgery lend themselves to robotic assistance, which has proven beneficial in providing safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g., instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study, using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics of the steady-hand eye robot and fiber Bragg grating sensor measurements. This is accomplished using a Kalman filtering approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from preoperative offline calibrations for stiffness.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- BDIS: Bayesian Dense Inverse Searching Method for Real-Time Stereo
Surgical Image Matching-
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Authors: Jingwei Song;Qiuchen Zhu;Jianyu Lin;Maani Ghaffari;
Pages: 1388 - 1406
Abstract: In stereoscope-based minimally invasive surgeries (MISs), dense stereo matching plays an indispensable role in 3-D shape recovery, AR, VR, and navigation tasks. Although numerous deep neural network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated dataset and the limitation of the task-specific pretrained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This article proposes the first CPU-level real-time prior-free stereo matching algorithm for general MIS tasks. We achieve an average $14-17$ Hz on $640 times 480$ images with a single-core CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular efficient large-scale stereo matching (ELAS) method. The patch-based fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4–5 times faster.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Collaborative Magnetic Manipulation via Two Robotically Actuated Permanent
Magnets-
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Authors: Giovanni Pittiglio;Michael Brockdorff;Tomas da Veiga;Joshua Davy;James Henry Chandler;Pietro Valdastri;
Pages: 1407 - 1418
Abstract: Magnetically actuated robots have proven effective in several applications, specifically in medicine. However, generating high actuating fields with a high degree of manipulability is still a challenge, especially when the application needs a large workspace to suitably cover a patient. The presented work discusses a novel approach for the control of magnetic field and field gradients using two robotically actuated permanent magnets. In this case, permanent magnets—relative to coil-based systems—have the advantage of larger field density without energy consumption. We demonstrate that collaborative manipulation of the two permanent magnets can introduce up to three additional Degrees of Freedom (DOFs) when compared to single permanent magnet approaches (five DOFs). We characterized the dual-arm system through the measurement of the fields and gradients and show accurate open-loop control with a 13.5% mean error. We then demonstrate how the magnetic DOFs can be employed in magnetomechanical manipulation, by controlling and measuring the wrench on two orthogonal magnets within the workspace, observing a maximum crosstalk of 6.1% and a mean error of 11.1%.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Large-Dimensional Multibody Dynamics Simulation Using Contact Nodalization
and Diagonalization-
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Authors: Jeongmin Lee;Minji Lee;Dongjun Lee;
Pages: 1419 - 1438
Abstract: In this article, we propose a novel multibody dynamics simulation framework that can efficiently deal with large-dimensionality and complementarity multicontact conditions. Typical contact simulation approaches require performing contact impulse fixed-point iteration, which has high time-complexity from large-size matrix factorization and multiplication, as well as susceptibility to ill-conditioned contact situations. To circumvent this, we propose a novel framework based on velocity fixed-point iteration (V-FPI), which, by utilizing a certain surrogate dynamics and contact nodalization (with virtual nodes), we achieve not only intercontact decoupling but also their interaxes decoupling (i.e., contact diagonalization) at each iteration step. This then enables us to one-shot/parallel-solve the contact problem during each V-FPI iteration-loop, while avoiding large-size/dense matrix inversion/multiplication, thereby, significantly speeding up the simulation time with improved convergence property. We theoretically show that the solution of our framework is consistent with that of the original problem and, further, elucidate mathematical conditions for the convergence of our proposed solver. Performance and properties of our proposed simulation framework are also demonstrated and experimentally validated for various large-dimensional/multicontact scenarios including deformable objects.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Reduced Euler-Lagrange Equations of Floating-Base Robots: Computation,
Properties, & Applications-
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Authors: Hrishik Mishra;Gianluca Garofalo;Alessandro Massimo Giordano;Marco De Stefano;Christian Ott;Andreas Kugi;
Pages: 1439 - 1457
Abstract: At first glance, a floating-base robotic system is a kinematic chain, and its equations of motion are described by the inertia-coupled dynamics of its shape and movable base. However, the dynamics embody an additional structure due to the momentum evolution, which acts as a velocity constraint. In prior works of robot dynamics, matrix transformations of the dynamics revealed a block-diagonal inertia. However, the structure of the transformed matrix of Coriolis/Centrifugal (CC) terms was not examined, and is the primary contribution of this article. To this end, we simplify the CC terms from robot dynamics and derive the analogous terms from geometric mechanics. Using this interdisciplinary link, we derive a two-part structure of the CC matrix, in which each partition is iteratively computed using a self-evident velocity dependency. Through this CC matrix, we reveal a commutative property, the velocity dependencies of the skew-symmetry property, the invariance of the shape dynamics to the basis of momentum, and the curvature as a matrix operator. Finally, we show the application of the proposed CC matrix structure through controller design and locomotion analysis.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows
-
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Authors: Qiangqiang Huang;Can Pu;Kasra Khosoussi;David M. Rosen;Dehann Fourie;Jonathan P. How;John J. Leonard;
Pages: 1458 - 1475
Abstract: This article presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- Lidar-Level Localization With Radar' The CFEAR Approach to Accurate, Fast,
and Robust Large-Scale Radar Odometry in Diverse Environments-
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Authors: Daniel Adolfsson;Martin Magnusson;Anas Alhashimi;Achim J. Lilienthal;Henrik Andreasson;
Pages: 1476 - 1495
Abstract: This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- ASRO-DIO: Active Subspace Random Optimization Based Depth Inertial
Odometry-
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Authors: Jiazhao Zhang;Yijie Tang;He Wang;Kai Xu;
Pages: 1496 - 1508
Abstract: High-dimensional nonlinear state estimation is at the heart of inertial-aided navigation systems (INS). Traditional methods usually rely on good initialization and find difficulty in handling large interframe transformations due to fast camera motion. We opt to tackle these challenges by solving the depth inertial odometry (DIO) problem with random optimization. To address the exponentially increased amount of candidate states sampled for the high-dimensional state space, we propose a highly efficient variant of random optimization based on the idea of active subspace. Our method identifies the active dimensions, which contribute most significantly to the decrease of the cost function in each iteration, and samples candidate states only within the corresponding subspace. This allows us to efficiently explore the 18D state space of DIO and achieve good optimality by sampling and evaluating only thousands of candidate states. Experiments show that our method attains highly robust and accurate DIO under fast camera motions and low light conditions, without needing a slow-motion warm-up for initialization.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Three-Dimensional Bearing-Only Target Following via Observability-Enhanced
Helical Guidance-
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Authors: Jianan Li;Zian Ning;Shaoming He;Chang-Hun Lee;Shiyu Zhao;
Pages: 1509 - 1526
Abstract: This paper studies the problem of air-to-air target following of micro aerial vehicles (MAVs) motivated by the application of defense against malicious MAVs. When the bearing of the target MAV has been measured by the onboard visual sensor of the pursuer MAV, the problem becomes three-dimensional (3-D) bearing-only target following, which has been rarely studied in the literature and faces some unique challenges. To solve this problem, we propose the following novel results. First, to estimate the motion of the target MAV from 3-D bearing measurements, we propose a new pseudo-linear Kalman filter, which has a concise expression and superior stability compared to the classic ones such as the extended Kalman filter and modified polar coordinate filter. Second, we propose a novel approach to analyze the observability of state estimation when only bearing information is available. While the existing approaches are applicable to 2-D and single-step time-horizon cases, ours can handle more general 3-D and multiple-step time-horizon cases. Third, based on the theoretical conclusion of our observability analysis, we design a new 3-D helical guidance law that can better exploit the additional degree of freedom in 3D. The guidance law is adapted to the quadcopter's dynamics and a low-level flight controller is designed based on geometric control. Numerical simulation results verify the superior performance of the proposed algorithms compared to the state-of-the-art ones. Flight experiments on real quadrotor platforms further show the effectiveness and robustness of the proposed algorithms in practice.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
-
- A Geometrically Exact Assumed Strain Modes Approach for the Geometrico-
and Kinemato-Static Modelings of Continuum Parallel Robots-
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Authors: Sébastien Briot;Frédéric Boyer;
Pages: 1527 - 1543
Abstract: There is a growing interest on the study of continuum parallel robots (CPRs) due to their higher stiffness and better dynamics capacities than serial continuum robots (SCRs). Several works have focused on the computation of their geometrico- and kinemato-static models that can be sorted into two main categories. Models based on the continuous Cosserat equations are very accurate but assessing elastic stability with them is tricky, and discretized models allow easily checking the elastic stability, but they require a large number of elastic variables to be accurate. In this article, we extend an approach based on assumed strain modes developed for the dynamics of SCRs to the statics of CPRs. This method is able to predict the robot configuration with an excellent accuracy with a very limited number of elastic variables, contrary to other discretization methods. The method is also more than 100 times faster than finite differences for a better prediction accuracy. Finally, it is possible to assess the robot elastic stability by only checking the Hessian of the potential energy as for any discretization method, thus making the analysis of this property simpler than for the continuous Cosserat model. All results are validated through simulations on two case studies.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Statics and Dynamics of Continuum Robots Based on Cosserat Rods and
Optimal Control Theories-
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Authors: Frédéric Boyer;Vincent Lebastard;Fabien Candelier;Federico Renda;Mazen Alamir;
Pages: 1544 - 1562
Abstract: This article explores the relationship between optimal control and Cosserat beam theory from the perspective of solving the forward and inverse dynamics (and statics as a subcase) of continuous manipulators and snake-like bioinspired locomotors. By invoking the principle of minimum potential energy and the Gauss principle of least constraint, it is shown that the quasi-static and dynamic evolutions of these robots are the solutions of optimal control problems in the space variable, which can be solved at each step (of loading or time) of a simulation with the shooting method. In addition to offering an alternative viewpoint on several simulation approaches proposed in the recent past, the optimal control viewpoint allows us to improve some of them while providing a better understanding of their numerical properties. The approach and its properties are illustrated through a set of numerical examples validated against a reference simulator.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Kinetostatic Modeling of Tendon-Driven Parallel Continuum Robots
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Authors: Sven Lilge;Jessica Burgner-Kahrs;
Pages: 1563 - 1579
Abstract: Tendon-driven parallel continuum robots (PCR) consist of multiple individual continuous kinematic chains, that are actuated in bending utilizing tendons routed along their backbones. This work derives and proposes a Cosserat rod based kinetostatic modeling framework for such parallel structures that allows for efficiently solving the forward, inverse and velocity kinetostatic problems. Using this model, the kinematic properties such as reachable workspace, singularities, manipulability, and compliance of tendon-driven PCR are studied in detail. Experiments are conducted using a real robotic prototype to validate the derived modeling approach. Overall, a median pose accuracy of 4.9 mm, corresponding to 3.4% of the continuum robots' lengths, and 6.2$^circ$ is achieved. The median of the model's computation time results in 0.51 s on standard computing hardware. Fast computations of below 100 ms can be achieved, if an appropriate initial guess for solving the kinetostatic model is available, making the model suitable for a range of different applications including optimization or control.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Shape Sensing of Flexible Robots Based on Deep Learning
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Authors: Xuan Thao Ha;Di Wu;Mouloud Ourak;Gianni Borghesan;Jenny Dankelman;Arianna Menciassi;Emmanuel Vander Poorten;
Pages: 1580 - 1593
Abstract: In this article, a deep learning method for the shape sensing of continuum robots based on multicore fiber bragg grating (FBG) fiber is introduced. The proposed method, based on an artificial neural network (ANN), differs from traditional approaches, where accurate shape reconstruction requires a tedious characterization of many characteristic parameters. A further limitation of traditional approaches is that they require either multiple fibers, whose location relative to the centerline must be precisely known (calibrated), or a single multicore fiber whose position typically coincides with the neutral line. The proposed method addresses this limitation and, thus, allows shape sensing based on a single multicore fiber placed off-center. This helps in miniaturizing and leaves the central channel available for other purposes. The proposed approach was compared to a recent state-of-the-art model-based shape sensing approach. A two-degree-of-freedom benchtop fluidics-driven catheter system was built to validate the proposed ANN. The proposed ANN-based shape sensing approach was evaluated on a 40-mm-long steerable continuum robot in both 3-D free-space and 2-D constrained environments, yielding an average shape sensing error of 0.24 and 0.49 mm, respectively. With these results, the superiority of the proposed approach compared to the recent model-based shape sensing method was demonstrated.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- An Atlas-Based Approach to Planar Variable-Structure Cable-Driven Parallel
Robot Configuration-Space Representation-
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Authors: Mitchell Rushton;Amir Khajepour;
Pages: 1594 - 1606
Abstract: Variable-structure cable-driven parallel robots (VSCR) are a new class of cable robots that are able to cover nonconvex installation spaces by permitting collisions between cables and fixed objects in the environment. In this article, we show how the configuration space of a general planar VSCR can be represented as an organized set of partially overlapping regions of constant structure. The benefit of this representation, which we refer to as the “structure atlas,” is that it allows any techniques from the established cable-driven parallel robot literature to be applied locally, greatly simplifying the modeling complexity associated with VSCRs. A complete method for how such a representation can be constructed is provided, which includes identifying the set of reachable kinematic structures for a given VSCR and the area where each structure is active. We then give specific examples of how this new representation can be used for performing VSCR workspace analysis and directly solving the VSCR inverse kinematics problem. Our results are demonstrated with the aid of simulated and experimental results.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Dynamic Parameter Identification of Serial Robots Using a Hybrid Approach
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Authors: Yanjiang Huang;Jianhong Ke;Xianmin Zhang;Jun Ota;
Pages: 1607 - 1621
Abstract: Model-based control can provide high-accuracy performance over position-based or velocity-based control. Therefore, to employ model-based control in industrial robots, it is important to estimate the dynamic parameters as accurately as possible. However, traditional estimation methods, such as least squares (LS), are not sufficiently accurate, and the feasibility of dynamic parameters cannot be guaranteed. In this article, an iterative hybrid least square (IHLS) algorithm is proposed to estimate the base parameters of industrial robots by dividing the identification processes into two loops. The inner loop integrates a linear matrix inequality with the semidefinite programming technique to guarantee physical feasibility and reuses the torque deviations between the measured torque and predicted torque to estimate the base parameters of the robot, while the outer loop substitutes the Stribeck friction model for the Coulomb-viscous friction model to estimate the joint friction torque. Moreover, backpropagation neural network (BPNN) is introduced to further estimate the joint friction torque based on the Stribeck friction model. Experiments are conducted on two industrial robots, and four methods are compared in dynamic parameter identification. Experimental results show that the hybrid approach of the IHLS algorithm with the BPNN has the best performance among the four methods.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Haptify: A Measurement-Based Benchmarking System for Grounded
Force-Feedback Devices-
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Authors: Farimah Fazlollahi;Katherine J. Kuchenbecker;
Pages: 1622 - 1636
Abstract: Grounded force-feedback (GFF) devices are an established and diverse class of haptic technology based on robotic arms. However, the number of designs and how they are specified make comparing devices difficult. We thus present Haptify, a benchmarking system that can thoroughly, fairly, and noninvasively evaluate GFF haptic devices. The user holds the instrumented device end-effector and moves it through a series of passive and active experiments. Haptify records the interaction between the hand, device, and ground with a seven-camera optical motion-capture system, a 60-cm-square custom force plate, and a customized sensing end-effector. We demonstrate six key ways to assess GFF device performance: workspace shape, global free-space forces, global free-space vibrations, local dynamic forces and torques, frictionless surface rendering, and stiffness rendering. We then use Haptify to benchmark two commercial haptic devices. With a smaller workspace than the 3D Systems Touch, the more expensive Touch X outputs smaller free-space forces and vibrations, smaller and more predictable dynamic forces and torques, and higher-quality renderings of a frictionless surface and high stiffness.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- Design, Control, and Experimental Evaluation of a Novel Robotic Glove
System for Patients With Brachial Plexus Injuries-
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Authors: Wenda Xu;Yunfei Guo;Cesar Bravo;Pinhas Ben-Tzvi;
Pages: 1637 - 1652
Abstract: This article presents the development of an exoskeleton glove system for people who suffer from brachial plexus injuries, aiming to assist their lost grasping functionality. The robotic system consists of a portable glove system and an embedded controller. The glove system consists of linear series elastic actuators, rotary series elastic actuators, and optimized finger linkages to provide imitated human motion to each finger and a coupled motion of the hand. The design principles and optimization strategies were investigated to balance functionality, portability, and stability. The model-based force control strategy compensated with a backlash model and model-free force control strategy are presented and compared. Results show that our proposed model-free control method achieves the goal of accurate force control. Finally, experiments were conducted with the prototype of the developed integrated exoskeleton glove system. Results from three subjects with 150 trials show that our proposed exoskeleton glove system has the potential to be used as a rehabilitation device for patients.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- A Time-Independent Control System for Natural Human Gait Assistance With a
Soft Exoskeleton-
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Authors: Xiaowei Tan;Bi Zhang;Guangjun Liu;Xingang Zhao;Yiwen Zhao;
Pages: 1653 - 1667
Abstract: When applying exoskeletons for walking assistance, one important consideration is to ensure that the users retain full control over the exoskeleton-provided assistance, which is quite limited in existing exoskeletons due to the absence of a suitable control system. In this article, a time-independent exoskeleton control system is developed based on a novel assistance profile generation method and an iterative force control method to enable continuous assistance adjustment. The assistance profile is formulated as a Gaussian function with a human state variable and can be updated online to adapt to different users. The proposed profile continuously self-adjusts along the movement of the user's leg, especially when users change their walking patterns. The proposed control system iteratively compensates for the force control lag and amplitude attenuation to enable precise tracking of the assistance profile during natural human walking. Experiments have been conducted using a soft exoskeleton on subjects with and without prior experience using an exoskeleton. The experimental results have shown the effectiveness of the proposed control system compared with a common time-dependent control system.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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- $^{++}$
:+An+Inclined-Stroke-Plane+Approach&rft.title=IEEE+Transactions+on+Robotics&rft.issn=1546-1904&rft.date=2023&rft.volume=39&rft.spage=1668&rft.epage=1684&rft.aulast=Pérez-Arancibia;&rft.aufirst=Ryan&rft.au=Ryan+M.+Bena;Xiufeng+Yang;Ariel+A.+Calderón;Néstor+O.+Pérez-Arancibia;">High-Performance Six-DOF Flight Control of the Bee
$^{++}$
: An
Inclined-Stroke-Plane Approach-
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Authors: Ryan M. Bena;Xiufeng Yang;Ariel A. Calderón;Néstor O. Pérez-Arancibia;
Pages: 1668 - 1684
Abstract: We present a new method for synthesizing and implementing high-performance six-degree-of-freedom ($boldsymbol{6}$-DOF) flight controllers for the Bee$^{++}$, an insect-scale flying robot driven by four independently-actuated flapping wings. Each wing of the Bee$^{++}$ is installed with a preset orientation such that the stroke plane generated during flight is inclined, thus enabling reliable roll, pitch, and yaw torque generation. Leveraging this capability, we propose a Lyapunov-based nonlinear control architecture that enables closed-loop position and attitude regulation and tracking. The control algorithms presented in this article simultaneously stabilize position and attitude by independently varying the wingstroke amplitudes of the four flapping wings of the Bee$^{++}$. We use this particular control architecture to exemplify the process of controller synthesis and real-time implementation; however, the aerodynamic design of the Bee$^{++}$ is compatible with a great variety of control structures and performance objectives. As a main result, we present the first set of experimental data demonstrating sustained and robust high-performance tracking of a $boldsymbol{6}$-DOF reference signal during flight at the insect scale, which has been a long-standing control problem in the field of flapping-wing microrobotics. Furthermore, using data obtained through a series of systematic flight tests, we show that the Bee$^{++}$ can achieve the highest $boldsymbol{6}$-DOF performance ever recorded for an insect-scale flapping-wing flying robot during sustained flight.
PubDate: April 2023
Issue No: Vol. 39, No. 2 (2023)
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