Towards Solving Multi-Agent Potential Field Local Minimas Through Imitation Learning

Artificial potential field is a widely used path-planning algorithm with robotics, simulation, and traffic management applications. One of the main drawbacks associated with this technique is its susceptibility to falling in local minimas, with this problem only being exacerbated when having multiple agents. This paper proposes a novel decentralized approach for solving local minima problems for APF methods in multi-agent settings. Our approach leverages local observations of the agents, enabling real-time decision-making without requiring full global knowledge. We evaluate our method against established baselines in a controlled simulation setting. Our results demonstrate that the proposed approach outperforms baselines in terms of agent success rate (reaching goals) by up to 10% while recording the lowest amount of collisions. Furthermore, our method exhibits good scalability with increasing numbers of agents and good stability shown by the low variance in the results obtained from the randomized test environments.