We consider the problem of multi-robot path planning in a complex, cluttered environment with the aim of reducing overall congestion in the environment, while avoiding any inter-robot communication or coordination. Such limitations may exist due to lack of communication or due to privacy restrictions (for example, autonomous vehicles may not want to share their locations or intents with other vehicles or even to a central server). The key insight that allows us to solve this problem is to stochastically distribute the robots across different routes in the environment by assigning them paths in different topologically distinct classes, so as to lower congestion and the overall travel time for all robots in the environment. We outline the computation of topologically distinct paths in a spatio-temporal configuration space and propose methods for the stochastic assignment of paths to the robots. A fast replanning algorithm and a potential field based controller allow robots to avoid collision with nearby agents while following the assigned path. Our simulation and experiment results show a significant advantage over shortest path following under such a coordination-free setup.

翻译：摘要：我们研究了在复杂杂乱环境中多机器人路径规划问题，旨在降低环境整体拥堵程度同时避免机器人间通信或协调。此类限制可能源于通信缺失或隐私约束（例如，自动驾驶车辆可能不愿与其他车辆或中央服务器共享自身位置或意图）。解决该问题的核心思路在于：通过为机器人分配不同拓扑等价类中的路径，使其随机分布于环境中的多条路线，从而降低拥堵并缩短环境中所有机器人的总通行时间。我们阐述了时空构型空间中拓扑不同路径的计算方法，并提出了路径随机分配给机器人的方案。通过快速重规划算法与基于势场的控制器，机器人能在沿分配路径运动时避免与邻近智能体发生碰撞。仿真与实验结果表明，在无需协调的框架下，本方法相较于单纯最短路径追踪具有显著优势。