Behavior-Based Herding Algorithm for Social Force Model Based Sheep Herd

Abstract

Inspired by real-world sheepdog herding behavior, in this paper, four behavior-based herding algorithms have been proposed for the social force model-based sheep herd. First, a basic behavior-based herding algorithm is designed where four types of critical sheep are rigorously defined. The decision of the sheepdog is made by constantly checking the positions of these four critical sheep. Then, on top of this basic herding algorithm, two extra mechanisms are considered to improve the performance of the basic herding algorithm, namely the dynamic far-end mechanism and the pausing mechanism, thus, forming the other three herding algorithms. The dynamic far-end mechanism helps to avoid the undesired circling behavior of the sheepdog around the destination area, while the pausing mechanism can greatly reduce the control cost of the sheepdog. To validate the effectiveness of the proposed herding algorithms, comprehensive tests have been conducted. The performance of the four algorithms is evaluated and compared from three aspects, namely, success rate, completion step, and control cost. Moreover, parameter analysis is provided to examine how different design parameters will affect the performance of the proposed algorithm. Finally, it is shown that when the size of the sheep herd increases, as expected, it takes more time and control effort to complete herding.