In this study, we propose a new sheepdog-inspired control method for a swarm of small unmanned aerial vehicles (UAVs), which predicts the swarm behavior while explicitly accounting for the motion constraints of real robots. Sheepdog-inspired guidance control refers to a framework in which a small number of navigator agents (sheepdog agents) indirectly drive a large number of autonomous agents (a flock of sheep agents) so as to steer the group toward a target position. In conventional studies on sheepdog-inspired guidance, both types of agents have typically been modeled as point masses, and the guidance law for the navigator agents has been designed using simple interaction vectors based on the instantaneous relative positions between the agents. However, when implementing such methods on real robots such as drones, it is necessary to consider each agent's motion constraints, including upper bounds on velocity and acceleration. Moreover, we argue that guidance can be made more efficient by predicting the future behavior of the autonomous swarm that is observable to the navigator agents. To this end, we propose a three-dimensional guidance control law based on behavior prediction of autonomous agents under motion constraints, inspired by the Dynamic Window Approach (DWA). At each control cycle, the navigator agent generates a set of feasible motion candidates that satisfy its motion constraints, and predicts the short-horizon swarm evolution using an internal model of the autonomous agents maintained within the navigator agent. The motion candidates are then evaluated according to criteria such as the progress velocity toward the target, the positioning strategy with respect to the swarm, and safety margins, and the optimal motion is selected to achieve safe and efficient guidance. Numerical simulation results demonstrate the effectiveness of the proposed guidance control law.

翻译：本研究提出了一种新颖的牧羊犬启发式控制方法，用于小型无人机集群，该方法在显式考虑真实机器人运动约束的同时预测集群行为。牧羊犬启发式制导控制是指：由少量导航代理（牧羊犬代理）间接驱动大量自主代理（羊群代理），引导整个群体朝向目标位置移动的框架。在传统牧羊犬启发式制导研究中，两类代理通常被建模为质点，导航代理的运动制导律基于代理间瞬时相对位置设计简单交互向量。然而，在无人机等真实机器人上实施此类方法时，必须考虑每个代理的运动约束，包括速度和加速度上限。此外，我们认为通过预测自主集群的可观测未来行为可提升制导效率。为此，受动态窗口法启发，我们提出了一种基于运动约束下自主代理行为预测的三维制导控制律。在每个控制周期，导航代理生成满足自身运动约束的可行运动候选集，并利用导航代理内部维护的自主代理内部模型预测短期集群演化。随后根据朝向目标的推进速度、相对于集群的定位策略及安全裕度等准则评估运动候选，选择最优运动以实现安全高效的制导。数值仿真结果验证了所提制导控制律的有效性。