Multiple Mobile Robots Planning Framework for Herding Non-Cooperative Target

Non-cooperative target herding is one of the concerns in the robotics field. For the non-cooperative target herding problem, a general planning framework is proposed to compel the target to the destination by a mobile robot team with pursuit, encirclement, and guidance operations. In the proposed planning framework, the encirclement strategy and guidance strategy are designed to deal with the unpredictability of the target. Firstly, the mobile robots approach the randomly moving target in the pursuit process. Secondly, the encirclement strategy is applied in the encirclement process to enable the mobile robot team to encircle the non-cooperative target. Finally, the guidance strategy is employed in the guidance process to enable the mobile robot team to form a favorable encirclement formation and compel the non-cooperative target to the destination. During the herding task, the mobile robots encircle the target or other mobile robots in a satellite-like motion. Moreover, two queues (orbiters and wanders) are maintained to change the formation adaptively, improving the flexibility of the framework. Various environments are designed to verify the effectiveness of the planning framework both in simulations and real-world experiments. Furthermore, the proposed framework is a general platform having great potential for various applications, where the numbers of mobile robots in the herding team are changeable, and different planning algorithms can be integrated into the framework. Note to Practitioners—The motivation for this paper is to propose a planning framework to efficiently herd the non-cooperative target in various scenarios for applications such as the maintenance of public safety and the facilitation of wildlife migration. The existing cooperative herding methods typically maintain relatively fixed formations, while the planning framework proposed in this paper introduces a satellite-based motion pattern and diverse formation transformation strategies, thereby enhancing formation flexibility and adaptability. Consequently, this framework can be effectively applied to challenging environments such as those with restricted areas and dynamic obstacles. Furthermore, the proposed planning framework allows for flexible adjustments in path planning methods and the number of mobile robots to meet various practical application requirements. Through a series of simulations and real-world experiments, the reliability and favorable performance of the proposed algorithm framework in practical applications are demonstrated.