Homotopy-aware Multi-agent Navigation via Distributed Model Predictive Control

TL;DR

This paper introduces a homotopy-aware distributed trajectory planning framework for multi-agent navigation that significantly reduces deadlocks and improves success rates in dense environments by combining global topological path planning with local MPC-based optimization.

Contribution

It presents a novel global path planning algorithm leveraging topological structures and a local MPC-based trajectory optimization with online replanning for multi-agent systems.

Findings

Success rate increased from 4%-13% to over 90% in dense scenarios.

Effectively mitigates deadlocks in obstacle-dense environments.

Incorporates time-aware homotopic properties for improved coordination.

Abstract

Multi-agent trajectory planning requires ensuring both safety and efficiency, yet deadlocks remain a significant challenge, especially in obstacle-dense environments. Such deadlocks frequently occur when multiple agents attempt to traverse the same long and narrow corridor simultaneously. To address this, we propose a novel distributed trajectory planning framework that bridges the gap between global path and local trajectory cooperation. At the global level, a homotopy-aware optimal path planning algorithm is proposed, which fully leverages the topological structure of the environment. A reference path is chosen from distinct homotopy classes by considering both its spatial and temporal properties, leading to improved coordination among agents globally. At the local level, a model predictive control-based trajectory optimization method is used to generate dynamically feasible and collision-free trajectories. Additionally, an online replanning strategy ensures its adaptability to dynamic environments. Simulations and experiments validate the effectiveness of our approach in mitigating deadlocks. Ablation studies demonstrate that by incorporating time-aware homotopic properties into the underlying global paths, our method can significantly reduce deadlocks and improve the average success rate from 4%-13% to over 90% in randomly generated dense scenarios.