SPF-EMPC Planner: A real-time multi-robot trajectory planner for complex environments with uncertainties

TL;DR

This paper presents SPF-EMPC, a real-time multi-robot trajectory planning method that effectively handles uncertainties and complex environments using a safe probability field and extended state model predictive control.

Contribution

It introduces a novel safe probability field combined with an extended state MPC to improve multi-robot navigation safety and real-time performance in uncertain, complex environments.

Findings

Success rate four times higher than state-of-the-art algorithms

Demonstrated real-time operation in physical experiments

Effectively handles uncertainties and complex environments

Abstract

In practical applications, the unpredictable movement of obstacles and the imprecise state observation of robots introduce significant uncertainties for the swarm of robots, especially in cluster environments. However, existing methods are difficult to realize safe navigation, considering uncertainties, complex environmental structures, and robot swarms. This paper introduces an extended state model predictive control planner with a safe probability field to address the multi-robot navigation problem in complex, dynamic, and uncertain environments. Initially, the safe probability field offers an innovative approach to model the uncertainty of external dynamic obstacles, combining it with an unconstrained optimization method to generate safe trajectories for multi-robot online. Subsequently, the extended state model predictive controller can accurately track these generated trajectories while considering the robots' inherent model constraints and state uncertainty, thus ensuring the practical feasibility of the planned trajectories. Simulation experiments show a success rate four times higher than that of state-of-the-art algorithms. Physical experiments demonstrate the method's ability to operate in real-time, enabling safe navigation for multi-robot in uncertain environments.