通过基于运动约束的动作预测来引导无人机群
Shepherding UAV Swarm with Action Prediction Based on Movement Constraints
AI总结：
本文提出了一种基于动态窗口方法的三维引导控制律，通过预测自主无人机群的行为来提高引导效率，考虑了运动约束下的可行运动候选生成和评估。
摘要：
在本研究中，我们提出了一种新的基于牧羊人理念的控制方法，用于小型无人驾驶航空器（UAV）群，该方法在显式考虑真实机器人运动约束的同时预测群体行为。牧羊人理念的引导控制是指一小群导航代理（牧羊人代理）间接驱动大量自主代理（羊群代理）以将群体引导至目标位置。在传统的牧羊人理念引导研究中，两种类型的代理通常都被建模为质点，导航代理的引导法则通常基于代理之间瞬时相对位置的简单交互向量设计。然而，当在真实机器人如无人机上实施此类方法时，必须考虑每个代理的运动约束，包括速度和加速度的上界。此外，我们认为通过预测导航代理可观察到的自主群的未来行为可以提高引导效率。为此，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引导控制律。在每个控制周期内，导航代理生成一组满足其运动约束的可行运动候选，并使用导航代理内部维护的自主代理内部模型预测短时间跨度的群体演化。然后根据诸如向目标的进度速度、相对于群体的位置策略和安全边距等标准评估这些运动候选，并选择最优运动以实现安全高效的引导。数值模拟结果展示了所提出引导控制律的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
