Shepherding algorithm for heterogeneous flock with model-based discrimination

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 consider the situation in which we are given a flock of agents consisting of normal agents to be guided and other variant agents. Under this situation, we propose a shepherding method for guiding the normal sheep. Specifically, 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.

Automated summary

The shepherding problem involves guiding a flock of agents to a destination using repulsion forces exerted by external agents. Most previous studies assume uniform dynamics of the agents to be guided, which may not hold in practical situations. This paper proposes a shepherding method that discriminates normal and variant agents based on their deviation from the predicted behavior of normal agents, using static and dynamic thresholds. Simulation results show the effectiveness of the proposed methods for different types of variant agents.