Obtaining Robust Control and Navigation Policies for Multi-Robot Navigation via Deep Reinforcement Learning

Multi-robot-navigation is one of the main challenges in mobile robotics. Multiple robots must be coordinated simultaneously to finish their task and have to navigate through a complex dynamic environment without causing collisions. One approach to enable the coordination of multi-robot navigation is prioritized planning, where robots plan their trajectories sequentially one after another. Prioritized planning algorithms tend to find a deadlock-free solution for route planning and centralized as well as decentralized planning solutions exist [1]. With a centralized approach all robots are coordinated by a single system, whereas navigation conflicts are resolved via communication between the robots in decentralized approaches. Prioritized path planning approaches tend to find solutions for scenarios with a high number of robots, while other approaches or reactive collisionavoidance algorithms like ORCA [2] fail. However, the main drawback of centralized approaches is the bad scalability as the planning complexity increases drastically with the number of robots and the size and complexity of the environment [3]. Additionally, a reliable and synchronized communication between the centralized planner and all robots is essential. Decentralized approaches often rely on communication between robots in order to share state information (e.g.