Towards Unstructured MAPF: Multi-Quadruped MAPF Demo
Multi-Agent Path Finding (MAPF) in its most broad perspective focuses on finding collision free paths for general teams of agents in a shared environment. Theoretically, MAPF methods could solve a variety of multi-agent problems. However, MAPF research primarily focuses on simplified warehouse domains, i.e., gridworld with discrete spaces, discrete timesteps, and point-mass agents without kinematic constraints. Thus, the perception of MAPF is tied closely to gridworld and its assumptions, which limits its attractiveness to more broad domains. However, there are several ways to extend MAPF methods past these classical assumptions. To this end, our demo shows how MAPF techniques can be used to plan for a team of quadrupeds. Our system plans in continuous space, in continuous time, with realistic footprints, and incorporates dynamics constraints.
@inproceedings{ VeerapaneniICAPS25,
author = "Rishi Veerapaneni and Nikhil Sobanbabu and Guanya Shi and Jiaoyang Li and Maxim Likhachev",
title = "Towards Unstructured MAPF: Multi-Quadruped MAPF Demo",
booktitle = "Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS)",
pages = "",
year = "2025",
doi = "",
}Abstract:
Multi-Agent Path Finding (MAPF) in its most broad perspective focuses on finding collision free paths for general teams of agents in a shared environment. Theoretically, MAPF methods could solve a variety of multi-agent problems. However, MAPF research primarily focuses on simplified warehouse domains, i.e., gridworld with discrete spaces, discrete timesteps, and point-mass agents without kinematic constraints. Thus, the perception of MAPF is tied closely to gridworld and its assumptions, which limits its attractiveness to more broad domains. However, there are several ways to extend MAPF methods past these classical assumptions. To this end, our demo shows how MAPF techniques can be used to plan for a team of quadrupeds. Our system plans in continuous space, in continuous time, with realistic footprints, and incorporates dynamics constraints.