Decentralized planning for multi-agent systems, such as fleets of robots in a search-and-rescue operation, is often constrained by limitations on how agents can communicate with each other. One such limitation is the case when agents can communicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods that guarantee safety is difficult in this case, as agents that are occluded from each other might not be able to communicate until it's too late to avoid a safety violation. In this paper, we develop a decentralized planning method that explicitly avoids situations where lack of visibility of other agents would lead to an unsafe situation. Building on top of an existing Rapidly-exploring Random Tree (RRT)-based approach, our method guarantees safety at each iteration. Simulation studies show the effectiveness of our method and compare the degradation in performance with respect to a clairvoyant decentralized planning algorithm where agents can communicate despite not being in LOS of each other.
Improved Discrete RRT for Coordinated Multi-robot Planning
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague（捷克理工大学信息学、机器人与控制论研究所）；Czech Technical University in Prague（捷克理工大学）
Accelerated RRT* and its evaluation on Autonomous Parking
Czech Technical University in Prague（捷克技术大学）；Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague（捷克技术大学信息学、机器人与控制论研究所）
School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）；Daniel Guggenheim School of Aerospace Engineering, Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院丹尼尔·古根海姆航空航天工程学院，机器人与智能机器研究所）
GVD-Exploration: An Efficient Autonomous Robot Exploration Framework Based on Fast Generalized Voronoi Diagram Extraction
Soochow University（苏州大学）；National University of Singapore（新加坡国立大学）；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）
Informed Sampling for Asymptotically Optimal Path Planning (Consolidated Version)
Carnegie Mellon University（卡内基梅隆大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）
