A Riemannian Metric for Geometry-Aware Singularity Avoidance by Articulated Robots
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）
Filip Marić, Luka Petrović, Marko Guberina, Jonathan Kelly, Ivan Petrović
备注：
12 pages, 12 figures
发表信息：
Robotics and Autonomous Systems, Vol. 145, pp. 103865, Nov. 2021
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, safety. We introduce a geometry-aware singularity index, defined using a Riemannian metric on the manifold of symmetric positive definite matrices, to provide a measure of proximity to singular configurations. We demonstrate that our index avoids some of the failure modes and difficulties inherent to other common indices. Further, we show that this index can be differentiated easily, making it compatible with local optimization approaches used for operational space control. Our experimental results establish that, for reaching and path following tasks, optimization based on our index outperforms a common manipulability maximization technique and ensures singularity-robust motions.
AVOID-JACK: Avoidance of Jackknifing for Swarms of Long Heavy Articulated Vehicles
Chair for Computational Intelligence, Otto-von-Guericke-University（计算智能系，奥托·冯·格里克大学）；Fraunhofer Institute for Transportation and Infrastructure Systems IVI（交通与基础设施系统研究所）
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）
Task-Space Singularity Avoidance for Control Affine Systems Using Control Barrier Functions
Department of Mechanical Engineering, University of Maryland, College Park（马里兰大学机械工程系，College Park分校）；Division of Magnetic Manipulation and Particle Research, Weinberg Medical Physics, Inc.（Weinberg医学物理公司磁操控与粒子研究部）；Department of Mechanical Engineering, Johns Hopkins University（约翰霍普金斯大学机械工程系）
A Relaxed Quadratic-Program-based Framework for Trajectory Tracking of Unicycle Robots with Singularity Avoidance
Department of Mechanical；Industrial Engineering at the New Jersey Institute of Technology (NJIT), NJ, USA；School of Engineering, Infrastructure；Sustainability De Montfort University, Leicester, LE1 9BH, UK
Manipulability Maximization Using Continuous-Time Gaussian Processes
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）
运动规划中的一个重大挑战是避免处于或靠近\emph{singular configurations}（\textit{singularities}），即，导致在任务空间中沿某些方向移动的能力丧失的关节配置。机器人系统的运动能力甚至在非常接近（即，一个奇点（Singularity）。在这项工作中，我们研究奇异避免在运动规划的背景下，找到轨迹，最大限度地减少接近奇异区域，受到约束。我们定义了一个基于可操作性的可能性与奇异性避免在连续的轨迹表示，然后我们最大限度地使用\textit{maximum a posteriori}（MAP）估计。将MAP问题视为因子图上的推理，我们使用内插状态的梯度信息来最大化轨迹的整体可操作性。实验数据的定性和定量分析表明，可操作性的增加，导致在平滑的轨迹与明显更灵巧的手臂配置。
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）
