This work considers a Motion Planning Problem with Dynamic Obstacles (MPDO) in 2D that requires finding a minimum-arrival-time collision-free trajectory for a point robot between its start and goal locations amid dynamic obstacles moving along known trajectories. Existing methods, such as continuous Dijkstra paradigm, can find an optimal solution by restricting the shape of the obstacles or the motion of the robot, while this work makes no such assumptions. Other methods, such as search-based planners and sampling-based approaches can compute a feasible solution to this problem but do not provide approximation bounds. Since finding the optimum is challenging for MPDO, this paper develops a framework that can provide tight lower bounds to the optimum. These bounds act as proxies for the optimum which can then be used to bound the deviation of a feasible solution from the optimum. To accomplish this, we develop a framework that consists of (i) a bi-level discretization approach that converts the MPDO to a relaxed path planning problem, and (ii) an algorithm that can solve the relaxed problem to obtain lower bounds. We also present numerical results to corroborate the performance of the proposed framework. These results show that the bounds obtained by our approach for some instances are up to twice tighter than a baseline approach showcasing potential advantages of the proposed approach.
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles
IBM Watson Research Center（IBM沃森研究中心）；Laboratoire d’Analyse et d’Architecture des Systèmes (LAAS), Institute of Mathematics, University of Toulouse（图卢兹大学数学研究所系统分析与架构实验室）；Robotics at Google（谷歌机器人部门）
Efficient RRT*-based Safety-Constrained Motion Planning for Continuum Robots in Dynamic Environments
Southern University of Science and Technology（南方科技大学）；Jiaxing Research Institute, Southern University of Science and Technology（南方科技大学嘉兴研究院）；City University of Hong Kong（香港城市大学）
Combining Safe Interval Path Planning and Constrained Path Following Control: Preliminary Results
National Research University Higher School of Economics（国家研究型高等经济大学）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Peoples’ Friendship University of Russia (RUDN University)（俄罗斯人民友谊大学（RUDN大学））；Bauman Moscow State Technical University（鲍曼莫斯科国立技术大学）
Whole-body motion planning and safety-critical control for aerial manipulation
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore（机械与航空航天工程学院，南洋理工大学，新加坡）；Department of Aerospace Engineering, Seoul National University, South Korea（航空航天工程系，首尔国立大学，韩国）
City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）
Collaborative-Online-Learning-Enabled Distributionally Robust Motion Control for Multi-Robot Systems
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China（自动化与智能感知学院，上海交通大学，上海，中国）；Department of Mechanical Engineering, University of Victoria, Victoria, Canada（机械工程系，维多利亚大学，维多利亚，加拿大）
