Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance

TL;DR

This paper introduces eIFP-MPC, an enhanced decentralized multi-robot path planning method that improves robustness, collision avoidance, and efficiency in dense, dynamic environments by integrating model predictive control with geometric planning.

Contribution

The paper presents eIFP-MPC, an optimized extension of the Iterative Forecast Planner that incorporates threat prioritization, cost-based via-point selection, and MPC for improved stability and robustness in multi-robot scenarios.

Findings

eIFP-MPC reduces oscillations and deadlocks in dense environments.

The method ensures collision-free trajectories in complex scenarios.

Simulations show improved efficiency and stability over previous approaches.

Abstract

The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scalable, and reactive solutions for multi-robot path planning in decentralized, communication-free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP-MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines threat prioritization using a time-to-collision heuristic, stabilizes path generation through cost-based via-point selection, and ensures dynamic feasibility by incorporating model predictive control (MPC) into the planning process. These enhancements are tightly integrated into the IFP to preserve its efficiency while improving its adaptability and stability. Extensive simulations across symmetric and high-density scenarios show that eIFP-MPC significantly reduces oscillations, ensures collision-free motion, and improves trajectory efficiency. The results demonstrate that geometric planners can be strengthened through optimization, enabling robust performance at scale in complex multi-agent environments.