水下微分对策：具有通信延迟的短时间目标搜索任务
Underwater Differential Game: Finite-Time Target Hunting Task with Communication Delay
摘要：
本文研究了一种无人水下机器人（UUV）群体目标搜索系统，以实现对高机动性目标的搜索。利用微分对策理论分析了有限时间内UUV与目标的作战策略。挑战在于UUV必须在考虑狩猎团队的一致性和目标的逃逸行为的情况下执行其控制策略。为了得到满足纳什均衡的稳定反馈控制策略，我们利用莱布尼茨公式构造了哈密顿函数。为了进一步考虑水下干扰和通信延迟，提出了一种改进的深度强化学习（DRL）方法，用于研究未知动态环境下的水下目标搜索任务。仿真结果表明，考虑通信时延时，水下干扰对系统有较大的影响。此外，一致性测试表明，UUV表现出更好的一致性与相对较小的干扰范围。
英文摘要：
This work considers designing an unmanned target hunting system for a swarm of unmanned underwater vehicles (UUVs) to hunt a target with high maneuverability. Differential game theory is used to analyze combat policies of UUVs and the target within finite time. The challenge lies in UUVs must conduct their control policies in consideration of not only the consistency of the hunting team but also escaping behaviors of the target. To obtain stable feedback control policies satisfying Nash equilibrium, we construct the Hamiltonian function with Leibniz's formula. For further taken underwater disturbances and communication delay into consideration, modified deep reinforcement learning (DRL) is provided to investigate the underwater target hunting task in an unknown dynamic environment. Simulations show that underwater disturbances have a large impact on the system considering communication delay. Moreover, consistency tests show that UUVs perform better consistency with a relatively small range of disturbances.
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。
