Distributed Multirobot Control for Non-Cooperative Herding

TL;DR

This paper develops and validates distributed multirobot herding algorithms using control barrier functions, enabling effective protection of high-value areas from sheep agents with real-world experiments involving multiple robots.

Contribution

It introduces two novel distributed herding algorithms, extending previous centralized methods with proofs of feasibility and optimality, validated through real-world experiments.

Findings

Distributed herding algorithms successfully herd up to five sheep.

Distributed algorithms outperform centralized approach in feasibility and optimality.

Real-world experiments confirm practical effectiveness of proposed methods.

Abstract

In this paper, we consider the problem of protecting a high-value area from being breached by sheep agents by crafting motions for dog robots. We use control barrier functions to pose constraints on the dogs' velocities that induce repulsion in the sheep relative to the high-value area. This paper extends the results developed in our prior work on the same topic in three ways. Firstly, we implement and validate our previously developed centralized herding algorithm on many robots. We show herding of up to five sheep agents using three dog robots. Secondly, as an extension to the centralized approach, we develop two distributed herding algorithms, one favoring feasibility while the other favoring optimality. In the first algorithm, we allocate a unique sheep to a unique dog, making that dog responsible for herding its allocated sheep away from the protected zone. We provide feasibility proof for this approach, along with numerical simulations. In the second algorithm, we develop an iterative distributed reformulation of the centralized algorithm, which inherits the optimality (i.e. budget efficiency) from the centralized approach. Lastly, we conduct real-world experiments of these distributed algorithms and demonstrate herding of up to five sheep agents using five dog robots.