Decentralized Multi-agent Path Selection Using Minimal Information

This work studies conflict avoidance between moving, non-communicating agents with minimum sensing information. While safety can be provided by reactive obstacle avoidance methods for holonomic systems, deadlock avoidance requires reasoning over different homotopic paths in cluttered scenes. A method to compute the “interaction cost” of a path is proposed, which considers only the neighboring agents’ observed positions. Minimizing the interaction cost in a prototypical challenge with two agents moving through two corridors from opposing sides guarantees the selection of non-conflicting paths. More complex scenes, however, are more challenging. This leads to a study of alternatives for decentralized path selection. Simulations indicate that following a “minimum-conflict” path given the other agents’ observed positions provides deadlock avoidance. A scheme that selects between the minimum-conflict path and a set of shortest paths given their interaction cost improves path quality while still achieving deadlock avoidance. Finally, learning to select between the minimum-conflict and one of the shortest paths allows agents to be adaptive to the behavior of their neighbors and can be achieved using regret minimization.