A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.
University of Toronto（多伦多大学）；University of Zagreb, Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
A significant challenge in motion planning is to avoid being in or near \emph{singular configurations} (\textit{singularities}), that is, joint configurations that result in the loss of the ability to move in certain directions in task space. A robotic system's capacity for motion is reduced even in regions that are in close proximity to (i.e., neighbouring) a singularity. In this work we examine singularity avoidance in a motion planning context, finding trajectories which minimize proximity to singular regions, subject to constraints. We define a manipulability-based likelihood associated wi…
Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning
Human-Robot Interfaces and Interaction(HRI2), Istituto Italiano Di Tecnologia (IIT)（人机交互（HRI2），意大利技术研究所（IIT））；Swiss Federal Technology Institute of Lausanne (EPFL)（洛桑联邦理工学院（EPFL））；Technical University of Munich（慕尼黑技术大学）；School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；School of Computer Science, University of Nottingham, UK（计算机科学学院，诺丁汉大学（英国））
Italian Institute of Technology（意大利技术研究院）；University of Genoa（热那亚大学）
This paper presents a simplified model-based trajectory optimization (TO) formulation for motion planning on quadruped mobile manipulators that carry heavy payload of known mass. The proposed payload-aware formulation simultaneously plans locomotion, payload manipulation and considers both robot and payload model dynamics while remaining computationally efficient. At the presence of heavy payload, the approach exhibits reduced leg outstretching (thus increased manipulability) in kinematically demanding motions due to the contribution of payload manipulation in the optimization. The framework's…
Singularity Avoidance with Application to Online Trajectory Optimization for Serial Manipulators
Automation and Control Institute (ACIN), TU Wien（维也纳工业大学自动化与控制研究所（ACIN））；Center for Vision, Automation and Control, AIT Austrian Institute of Technology（奥地利技术研究所（AIT）视觉、自动化与控制中心）
NTNU, Norwegian University of Science and Technology（挪威科技大学）；SINTEF DIGITAL（挪威科技工业研究院数字部门）
A Python module for rapid prototyping of constraint-based closed-loop inverse kinematics controllers is presented. The module allows for combining multiple tasks that are resolved with a quadratic, nonlinear, or model predictive optimization-based approach, or a set-based task-priority inverse kinematics approach. The optimization-based approaches are described in relation to the set-based task approach, and a novel multidimensional "in tangent cone" function is presented for set-based tasks. A ROS component is provided, and the controllers are tested with matching a pose using either transfor…
TU Wien（维也纳工业大学）；University of Liverpool（利物浦大学）；Austrian Institute of Technology GmbH (AIT)（奥地利技术研究院有限公司）
Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-specific demands helps to improve the robustness and to increase the success rate when driving the manipulator from its current configuration towards a desired pose. This is necessary, especially in high-dynamic tasks like catching objects in mid-flights. To compute a suitable target configuration in the joint space for a given target pose in the t…
Trajectory Planning and Control for Robotic Magnetic Manipulation
Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University（电气工程与自动化系，电气工程学院，阿alto大学）；Faculty of Engineering and Natural Sciences, Automation Technology and Mechanical Engineering, Tampere University（工程与自然科学学院，自动化技术与机械工程，塔尔基大学）
Ellipsoid-based manipulability measures are often used to characterize the force/velocity task-space capabilities of robots. While computationally simple, this approach largely approximate and underestimate the true capabilities. Force/velocity polytopes appear to be a more appropriate representation to characterize robot's task-space capabilities. However, due to the computational complexity of the associated vertex search problem, the polytope approach is mostly restricted to offline use, e.g. as a tool aiding robot mechanical design, robot placement in work-space and offline trajectory plan…
Multimodal Control of Manipulators: Coupling Kinematics and Vision for Self-Driving Laboratory Operations
1 Department of Electronics Systems, Aalborg University, Denmark；3 Extreme Robotics Laboratory, School of Metallurgy \& Materials, University of Birmingham, Birmingham, United Kingdom
Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the ite…
A Task-Driven, Planner-in-the-Loop Computational Design Framework for Modular Manipulators
Humanoids and Human Centered Mechatronics Research Line, Italian Institute of Technology (IIT) Genoa, Italy（意大利理工学院（IIT）热那亚人类与以人为中心的机电研究组）；Department of Industrial Engineering, University of Trento, Trento, Italy（特伦托大学工业工程系）
Australian Centre for Robotic Vision (ACRV)（澳大利亚机器人视觉中心）；Queensland University of Technology（昆士兰科技大学）
We present NEO, a fast and purely reactive motion controller for manipulators which can avoid static and dynamic obstacles while moving to the desired end-effector pose. Additionally, our controller maximises the manipulability of the robot during the trajectory, while avoiding joint position and velocity limits. NEO is wrapped into a strictly convex quadratic programme which, when considering obstacles, joint limits, and manipulability on a 7 degree-of-freedom robot, is generally solved in a few ms. While NEO is not intended to replace state-of-the-art motion planners, our experiments show th…
PhysV2A: Reachability-Gated and Semantic-Mask-Constrained Feasibility Completion for Video-to-Robot Manipulation
School of Automation, Guangdong University of Technology（广东工业大学自动化学院）；University of Liverpool（利物浦大学）；Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算学系）
Biasing the Driving Style of an Artificial Race Driver for Online Time-Optimal Maneuver Planning
Department of Industrial Engineering, University of Trento（工业工程系，特伦托大学）；Department of Electrical and Information Engineering, Politecnico di Bari（电气与信息工程系，巴里理工学院）；Professorship of Autonomous Vehicle Systems, Technical University of Munich（自主车辆系统教授职位，慕尼黑技术大学）
Safe Obstacle-Free Guidance of Space Manipulators in Debris Removal Missions via Deep Reinforcement Learning
Embodied Learning and Intelligence for eXploration and Innovative soft Robotics (ELIXIR) Lab, Toronto Metropolitan University, Toronto, ON, Canada（体现学习与智能探索与创新软机器人（ELIXIR）实验室，多伦多 Metropolitan 大学，多伦多，ON，加拿大）
RCM-Constrained Manipulator Trajectory Tracking Using Differential Kinematics Control
Center for Artificial Intelligence and Robotics (CAIR), New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校人工智能与机器人中心（CAIR））；Electrical Engineering, NYUAD（纽约大学阿布扎比分校电气工程系）
University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；University of Zagreb Faculty of Electrical Engineering and Computing（萨格勒布大学电气工程与计算学院）
Articulated robots such as manipulators increasingly must operate in uncertain and dynamic environments where interaction (with human coworkers, for example) is necessary. In these situations, the capacity to quickly adapt to unexpected changes in operational space constraints is essential. At certain points in a manipulator's configuration space, termed singularities, the robot loses one or more degrees of freedom (DoF) and is unable to move in specific operational space directions. The inability to move in arbitrary directions in operational space compromises adaptivity and, potentially, saf…
