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：我们研究了在复杂拥挤环境中多机器人路径规划的问题，目标是在避免机器人间任何通信或协调的情况下降低环境整体拥堵程度。这种限制可能源于通信缺失或隐私限制（例如，自动驾驶车辆可能不愿与其他车辆甚至中央服务器共享其位置或意图）。解决该问题的关键思路是：通过将机器人分配到不同拓扑类别的路径上，以随机方式使它们分布在不同路径中，从而降低拥堵并减少环境中所有机器人的总行驶时间。我们概述了在时空构型空间中计算拓扑不同路径的方法，并提出了路径的随机分配方案。一种快速重规划算法和基于势场的控制器使得机器人能在沿指定路径行驶时避免与附近代理发生碰撞。仿真与实验结果表明，在这种无协调设置下，我们的方法相比最短路径跟随具有显著优势。