Shepherding Heterogeneous Flock with Model-Based Discrimination

TL;DR

This paper introduces a model-based discrimination approach for shepherding heterogeneous agent flocks, effectively guiding normal agents while identifying variants, even with model inaccuracies, outperforming traditional methods.

Contribution

It proposes a novel shepherding method that discriminates between normal and variant agents using static and dynamic thresholds based on model predictions.

Findings

The proposed methods outperform conventional shepherding in simulations.

Discrimination accuracy remains high despite model inaccuracies.

Both static and dynamic threshold approaches are effective.

Abstract

The problem of guiding a flock of agents to a destination by the repulsion forces exerted by a smaller number of external agents is called the shepherding problem. This problem has attracted attention due to its potential applications, including diverting birds away for preventing airplane accidents, recovering spilled oil in the ocean, and guiding a swarm of robots for mapping. Although there have been various studies on the shepherding problem, most of them place the uniformity assumption on the dynamics of agents to be guided. However, we can find various practical situations where this assumption does not necessarily hold. In this paper, we propose a shepherding method for a flock of agents consisting of normal agents to be guided and other variant agents. In this method, the shepherd discriminates normal and variant agents based on their behaviors' deviation from the one predicted by the potentially inaccurate model of the normal agents. As for the discrimination process, we propose two methods using static and dynamic thresholds. Our simulation results show that the proposed methods outperform a conventional method for various types of variant agents.