Multi-Agent Feedback Motion Planning using Probably Approximately Correct Nonlinear Model Predictive Control

TL;DR

This paper introduces a distributed multi-agent motion planning method using PAC-NMPC that effectively manages uncertainties for formation control and obstacle avoidance in cluttered environments.

Contribution

It presents a novel distributed PAC-NMPC framework with a terminal cost function for robust multi-agent formation control under uncertainty.

Findings

Performs comparably to centralized methods in simulations.

Improves robustness under high measurement noise.

Scales to complex dynamical systems.

Abstract

For many tasks, multi-robot teams often provide greater efficiency, robustness, and resiliency. However, multi-robot collaboration in real-world scenarios poses a number of major challenges, especially when dynamic robots must balance competing objectives like formation control and obstacle avoidance in the presence of stochastic dynamics and sensor uncertainty. In this paper, we propose a distributed, multi-agent receding-horizon feedback motion planning approach using Probably Approximately Correct Nonlinear Model Predictive Control (PAC-NMPC) that is able to reason about both model and measurement uncertainty to achieve robust multi-agent formation control while navigating cluttered obstacle fields and avoiding inter-robot collisions. Our approach relies not only on the underlying PAC-NMPC algorithm but also on a terminal cost-function derived from gyroscopic obstacle avoidance. Through numerical simulation, we show that our distributed approach performs on par with a centralized formulation, that it offers improved performance in the case of significant measurement noise, and that it can scale to more complex dynamical systems.