Game-theoretic multi-agent motion planning in a mixed environment

Abstract

The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in a mixed environment. To address this challenge, this paper presents an interaction-aware motion planning approach based on game theory in a receding-horizon manner. Leveraging the framework provided by dynamic potential games for handling the interactions among agents, this approach formulates the multi-agent motion planning problem as a differential potential game, highlighting the effectiveness of constrained potential games in facilitating interactive motion planning among agents. Furthermore, online learning techniques are incorporated to dynamically learn the unknown preferences and models of humans or human-controlled robots through the analysis of observed data. To evaluate the effectiveness of the proposed approach, numerical simulations are conducted, demonstrating its capability to generate interactive trajectories for all agents, including humans and human-controlled agents, operating within the mixed environment. The simulation results illustrate the effectiveness of the proposed approach in handling the complexities of multi-agent motion planning in real-world scenarios.