Cooperative Hunting Method for Multiple-Agents Using Differential Games Based on Escape Angle
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摘要: 针对单个对抗性目标主动逃逸问题, 利用微分博弈理论建立了多智能体协同围捕问题博弈模型, 在含有距离项支付函数中引入由逃逸角构成的合围项, 从而降低目标中途逃逸的概率; 然后将围捕问题转换为求解可实现策略的优化问题, 利用粒子群优化算法求解满足纳什均衡的最优策略, 仿真和湖上试验结果均证明了基于粒子群的微分博弈围捕算法的有效性。Abstract: Aiming at the problem that the adversarial single-target actively escapes, a game model of multi-agent cooperative encirclement problem is established using differential game (DG) theory. Introducing The escape angle correlation term is introduced into the traditional payment function which includes the distance cost such that the escape probability of the target is reduced; At the same time, the encirclement problem is converted into multiple groups of pursuit and escape countermeasures, and Particle Swarm Optimization (PSO) algorithm is used to solve the optimal strategy that satisfies the Nash equilibrium. The simulation results show the effectiveness of the algorithm based on DG-PSO fusion. Then experiments based on multiple autonomous surface vehicles shows that the designed hunting algorithm is effective.
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图 1 二维直角坐标系追逃几何模型
Figure 1. Two-dimensional Cartesian coordinate system pursuit-and-escape geometry
图 3 极坐标系下逃逸角示意图
Figure 3. Schematic diagram of the escape angle in a polar coordinate system
图 4 围捕者使邻居逃逸角“均匀化”示意图
Figure 4. Schematic diagram of the round-up "homogenizing" the escape angle of the neighbor
图 9 不同算法追逃最小距离对比图
Figure 9. Comparison chart of the minimum distance of pursuit and escape
图 12 ASV航行器的Ardupilot底层控制代码架构
Figure 12. The underlying control code architecture of the Ardupilot of the ASV vehicle
表 1 航行器性能参数表
Table 1. Vehicle performance parameters
性能指标 数值 单位 外观尺寸 510×180×115 mm·mm·mm 最大速度 2.2 m/s 最大航时 50 min 电池容量 5 100 mA·h 最大续航里程 4.5 km 最大通讯距离 1.2 km 
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