Cooperative pathfinding is a problem of finding a set of non-conflicting trajectories for a number of mobile agents. Its applications include planning for teams of mobile robots, such as autonomous aircrafts, cars, or underwater vehicles. The state-of-the-art algorithms for cooperative pathfinding typically rely on some heuristic forward-search pathfinding technique, where A* is often the algorithm of choice. Here, we propose MA-RRT*, a novel algorithm for multi-agent path planning that builds upon a recently proposed asymptotically-optimal sampling-based algorithm for finding single-agent shortest path called RRT*. We experimentally evaluate the performance of the algorithm and show that the sampling-based approach offers better scalability than the classical forward-search approach in relatively large, but sparse environments, which are typical in real-world applications such as multi-aircraft collision avoidance.
Multi-goal Rapidly Exploring Random Tree with Safety and Dynamic Constraints for UAV Cooperative Path Planning
Vietnam National University, Hanoi（越南河内国家大学）；Thai Nguyen University of Technology, Thai Nguyen University（太原大学太原工业学院）；University of Transport and Communications（交通通信大学）；Fulbright University Vietnam（越南富布赖特大学）
Automatic Algorithm Selection In Multi-agent Pathfinding
University of Alberta（阿尔伯塔大学）；Washington University in St. Louis（圣路易斯华盛顿大学）；Universidad Andres Bello（安德烈斯·贝略大学）；University of Southern California（南加州大学）
Hangzhou International Innovation Institute, Beihang University, China（北京航空航天大学杭州国际创新研究院）；CoreControl Inc, Hangzhou, China（杭州核心控制公司）；Department of Mechanical Engineering, National University of Singapore, Singapore（新加坡国立大学机械工程系）
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid Environments
Moscow Institute of Physics and Technology（莫斯科物理技术学院）；Moscow Aviation Institute（莫斯科航空学院）；Artificial Intelligence Research Institute FRC CSC RAS（俄罗斯科学院联邦科学研究中心计算机科学与控制研究所人工智能研究院）
Conflict-Based Lazy Search for Fast Multi-Manipulator Planning
Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai Institute of Intelligent Science and Technology, the State Key Laboratory of Autonomous Intelligent Unmanned Systems, and Frontiers Science Center for Intelligent Autonomous Systems of Ministry of Education, Tongji University（上海智能无人系统科学中心，上海智能科学与技术研究院，自主智能无人系统国家重点实验室，同济大学智能自主系统前沿科学中心）；Department of Automation, Shanghai Jiao Tong University（上海交通大学自动化系）；School of Aerospace Engineering and Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院航空航天工程学院和机器人与智能机器研究所）
UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；Vanke School of Public Health, Institute for Healthy China, Tsinghua University（万科公共卫生学院，中国健康研究院，清华大学）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）；School of Civil Engineering, Tsinghua University（土木工程学院，清华大学）
Multi-UAV Formation Cooperative Obstacle Avoidance and Adaptive Shape Deformation Control in Complex Environments Based on BI-APF-RRT and Affine Transformation
College of Marine Information Engineering（海洋信息工程学院）
