Path planning for swarms in dynamic environments by combining probabilistic roadmaps and potential fields

This paper presents a path-planning approach to enable a swarm of robots move to a goal region while avoiding collisions with static and dynamic obstacles. To provide scalability and account for the complexity of the interactions in the swarm, the proposed approach combines probabilistic roadmaps with potential fields. The underlying idea is to provide the swarm with a series of intermediate goals which are obtained by constructing and searching a roadmap of likely collision-free guides. As the swarm moves from one intermediate goal to the next, it relies on potential fields to quickly react and avoid collisions with static and dynamic obstacles. Potential fields are also used to ensure that the swarm moves in cohesion. When the swarm deviates or is unable to reach the planned intermediate goals due to interference from the dynamic obstacles, the roadmap is searched again to provide alternative guides. Experiments conducted in simulation demonstrate the efficiency and scalability of the approach.