Heuristic Predictive Control for Multi-Robot Flocking in Congested Environments

TL;DR

This paper introduces a heuristic predictive control method for multi-robot flocking in crowded environments, utilizing bio-inspired potential functions and a Gibbs Random Field to enhance safety and efficiency.

Contribution

It proposes a novel distributed control approach based on a Gibbs Random Field, incorporating a gradient-based heuristic for faster computation and improved collision avoidance.

Findings

The heuristic control significantly speeds up computation.

The method outperforms existing flocking controls in simulations.

Real UAV experiments validate the approach's effectiveness.

Abstract

Multi-robot flocking possesses extraordinary advantages over a single-robot system in diverse domains, but it is challenging to ensure safe and optimal performance in congested environments. Hence, this paper is focused on the investigation of distributed optimal flocking control for multiple robots in crowded environments. A heuristic predictive control solution is proposed based on a Gibbs Random Field (GRF), in which bio-inspired potential functions are used to characterize robot-robot and robot-environment interactions. The optimal solution is obtained by maximizing a posteriori joint distribution of the GRF in a certain future time instant. A gradient-based heuristic solution is developed, which could significantly speed up the computation of the optimal control. Mathematical analysis is also conducted to show the validity of the heuristic solution. Multiple collision risk levels are designed to improve the collision avoidance performance of robots in dynamic environments. The proposed heuristic predictive control is evaluated comprehensively from multiple perspectives based on different metrics in a challenging simulation environment. The competence of the proposed algorithm is validated via the comparison with the non-heuristic predictive control and two existing popular flocking control methods. Real-life experiments are also performed using four quadrotor UAVs to further demonstrate the efficiency of the proposed design.