Decentralized Uncertainty-Aware Multi-Agent Collision Avoidance with Model Predictive Path Integral

TL;DR

This paper introduces a decentralized, uncertainty-aware multi-agent collision avoidance method combining MPPI with probabilistic safety constraints, enabling safe navigation with noisy observations and outperforming existing approaches in simulations.

Contribution

It presents a novel integration of MPPI with probabilistic safety constraints via Second-Order Cone Programming for decentralized multi-agent navigation under uncertainty.

Findings

Outperforms state-of-the-art methods like ORCA-DD and B-UAVC in simulations.

Achieves high success rates in densely populated environments.

Validated in Gazebo simulator demonstrating practical applicability.

Abstract

Decentralized multi-agent navigation under uncertainty is a complex task that arises in numerous robotic applications. It requires collision avoidance strategies that account for both kinematic constraints, sensing and action execution noise. In this paper, we propose a novel approach that integrates the Model Predictive Path Integral (MPPI) with a probabilistic adaptation of Optimal Reciprocal Collision Avoidance. Our method ensures safe and efficient multi-agent navigation by incorporating probabilistic safety constraints directly into the MPPI sampling process via a Second-Order Cone Programming formulation. This approach enables agents to operate independently using local noisy observations while maintaining safety guarantees. We validate our algorithm through extensive simulations with differential-drive robots and benchmark it against state-of-the-art methods, including ORCA-DD and B-UAVC. Results demonstrate that our approach outperforms them while achieving high success rates, even in densely populated environments. Additionally, validation in the Gazebo simulator confirms its practical applicability to robotic platforms. A source code is available at http://github.com/PathPlanning/MPPI-Collision-Avoidance.