Homotopy-Guided Potential Games for Congestion-Aware Navigation

TL;DR

This paper introduces a homotopy-guided potential game framework for multi-agent navigation that improves efficiency and safety by exploring topologically distinct paths and enforcing homotopy constraints.

Contribution

It unifies homotopy-based path planning with potential games, enabling agents to select congestion-free strategies in a receding-horizon setting with improved robustness.

Findings

Simulations show faster completion times and better safety metrics.

The method adapts to irrational behaviors by switching equilibria.

Hardware trials demonstrate robustness in real-world scenarios.

Abstract

We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the interdependence of agents' future actions. We propose a unified framework that leverages homotopy classes as structured strategy sets within a receding-horizon setup. At each planning stage, a deterministic homotopy planner generates topologically distinct paths for each agent, conditioned on the joint configuration. To avoid intractable growth of candidate paths, we propose a simple heuristic filtering step that selects a top-$K$ subset of the most suitable congestion-free joint strategies to ensure computational tractability. These serve as initializations for a potential game that enforces homotopy-consistent constraints and yields a generalized open-loop Nash equilibrium (OLNE), with penalties discouraging abrupt strategy shifts in a receding-horizon setting. Simulations with three agents demonstrate improved efficiency (faster completion) and enhanced safety (greater inter-agent clearance, leading to reduced congestion) compared to a local baseline and NH-ORCA that do not reason about homotopies. Hardware trials with two robots and one human demonstrate robustness to irrational behaviors, where our method adapts by switching to alternative feasible equilibria while the baseline game fails.