Adaptive Multi-robot Implicit Control of Heterogeneous Herds

TL;DR

This paper introduces an innovative implicit control method for robotic herders to effectively manage heterogeneous, nonlinear evader groups, incorporating adaptive strategies, caging techniques, and distributed estimation, validated through simulations and experiments.

Contribution

It proposes a novel implicit control approach with stability guarantees and a caging technique for herding heterogeneous, nonlinear evaders, addressing uncertainties and measurement limitations.

Findings

Effective herding of nonlinear, heterogeneous evaders demonstrated

Adaptive control law with stability guarantees developed

Successful validation through simulations and experiments

Abstract

This paper presents a novel control strategy to herd groups of non-cooperative evaders by means of a team of robotic herders. In herding problems, the motion of the evaders is typically determined by strongly nonlinear and heterogeneous reactive dynamics, which makes the development of flexible control solutions a challenging problem. In this context, we propose Implicit Control, an approach that leverages numerical analysis theory to find suitable herding inputs even when the nonlinearities in the evaders' dynamics yield to implicit equations. The intuition behind this methodology consists in driving the input, rather than computing it, towards the unknown value that achieves the desired dynamic behavior of the herd. The same idea is exploited to develop an adaptation law, with stability guarantees, that copes with uncertainties in the herd's models. Moreover, our solution is completed with a novel caging technique based on uncertainty models and Control Barrier Functions (CBFs), together with a distributed estimator to overcome the need of complete perfect measurements. Different simulations and experiments validate the generality and flexibility of the proposal.