研究防御者如何拦截连续来袭的无人机，基于运动约束优化拦截策略。
Target Defense against Sequentially Arriving Intruders: Algorithm for Agents with Dubins Dynamics
- 分阶段设计策略：信息不足时保守应对，信息充足时主动拦截。
- 理论证明能捕获超过80%的入侵者，且无限序列下捕获率趋近于稳定值。
- 适用于无人机防御、智能交通等需连续响应的动态博弈场景。
我们研究了一种目标防御问题的变体，即单个防御者需拦截一系列连续到达的入侵者。防御者与入侵者均具有非完整动力学特性。入侵者的目的是在不被捕捉的情况下突破目标边界，而防御者的目标是尽可能多地捕获入侵者。当一个入侵者被击落或突破后，下一个入侵者会随机出现在围绕目标的固定圆周上，因此防御者在一轮结束后的最终位置即为下一轮的起始位置。我们将一次入侵者-防御者交锋分为两个阶段：部分信息和完全信息，依据双方可用信息的多少。通过引入杜宾路径（Dubins path）和守卫弧（guarding arc）的概念，分析了防御者对入侵者的可捕获性，并量化了有限和无限序列下入侵者的被捕率。最后，通过蒙特卡洛类随机实验验证了理论结果的正确性。
原文摘要 · Abstract (English)
We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. Both the defender and the intruders have non-holonomic dynamics. The intruders' objective is to breach the target perimeter without being captured by the defender, while the defender's goal is to capture as many intruders as possible. After one intruder breaches or is captured, the next appears randomly on a fixed circle surrounding the target. Therefore, the defender's final position in one game becomes its starting position for the next. We divide an intruder-defender engagement into two phases, partial information and full information, depending on the information available to the players. We address the capturability of an intruder by the defender using the notions of Dubins path and guarding arc. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders. Finally, the theoretical results are verified through numerical examples using Monte-Carlo-type random trials of experiments.
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