| 摘要: |
| The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled
robots are present in amixed environment. To address this challenge, this paper presents an interaction-awaremotion 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. |
| 关键词: Motion planning · Differential potential game · Multi-agent systems · Constrained potential game |
| DOI：https://doi.org/10.1007/s11768-024-00207-9 |
|
| 基金项目:This work was supported by the ASTAR under its “RIE2025 IAF-PP Advanced ROS2-native Platform Technologies for Cross sectorial Robotics Adoption (M21K1a0104)” programme. |
|
| Game-theoreticmulti-agent motion planning in amixed environment |
| Xiaoxue Zhang1,Lihua Xie1 |
| (1 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore City 639798, Singapore) |
| Abstract: |
| The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled
robots are present in amixed environment. To address this challenge, this paper presents an interaction-awaremotion 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. |
| Key words: Motion planning · Differential potential game · Multi-agent systems · Constrained potential game |
