宋利飞, 徐凯凯, 史晓骞, 等. 多无人艇协同围捕智能逃跑目标方法研究[J]. 中国舰船研究, 2023, 18(1): 52–59. doi: 10.19693/j.issn.1673-3185.02974
引用本文: 宋利飞, 徐凯凯, 史晓骞, 等. 多无人艇协同围捕智能逃跑目标方法研究[J]. 中国舰船研究, 2023, 18(1): 52–59. doi: 10.19693/j.issn.1673-3185.02974
SONG L F, XU K K, SHI X Q, et al. Multiple USV cooperative algorithm method for hunting intelligent escaped targets[J]. Chinese Journal of Ship Research, 2023, 18(1): 52–59. doi: 10.19693/j.issn.1673-3185.02974
Citation: SONG L F, XU K K, SHI X Q, et al. Multiple USV cooperative algorithm method for hunting intelligent escaped targets[J]. Chinese Journal of Ship Research, 2023, 18(1): 52–59. doi: 10.19693/j.issn.1673-3185.02974

多无人艇协同围捕智能逃跑目标方法研究

Multiple USV cooperative algorithm method for hunting intelligent escaped targets

  • 摘要:
      目的  针对无人艇协同围捕过程中逃跑目标具备智能性,现有无人艇策略难以围捕成功的问题,提出一种基于双层切换策略的多无人艇协同围捕算法。
      方法  第1层围捕策略采用改进势点法,以无人艇与势点的总直线距离最小为优化目标,采用匈牙利算法为无人艇动态分配势点,并采用人工势场法实现无人艇的协同避碰;第2层围捕策略利用了阿波罗尼奥斯圆的性质,在两艘无人艇前往逃跑目标的目标点进行拦截,剩余无人艇运动方向保持与逃跑目标相同,以不断缩紧包围区域;为应对逃跑目标不同的逃跑方式,第1层围捕策略和第2层围捕策略可互相转化。
      结果  仿真实验表明,该算法相较于顺序分配势点算法和极角分配势点算法,围捕时间更少或持平,证明了该算法的有效性和先进性。
      结论  该多无人艇协同双层围捕算法,对具备典型智能性的逃跑目标具有围捕效果。

     

    Abstract:
      Objectives  A multiple unmanned surface vehicle (USV) cooperative hunting algorithm based on the double layer switching strategy is proposed to cope with the difficulties of USVs in hunting intelligent escaped targets.
      Methods  Specifically, the first hunting strategy adopts the improved potential point method. The Hungarian algorithm is employed in order to dynamically allocate potential points for USVs, and the optimization goal is applied to minimize the total linear distance between USVs and potential points. In this process, the artificial potential field method is used to achieve cooperative collision avoidance. The second hunting strategy takes advantage of the nature of the Apollonius circle to tighten the surrounding area, i.e. two USVs go to the target point of the escaped target to intercept it, while the remaining USVs maintain the same direction as the escaped target. Moreover, in order to deal with the different escape strategies of targets, the first and second layers of hunting strategy can be transformed into each other.
      Results  Numerical simulation shows that the proposed algorithm can reduce the hunting time to less than or equal to that of the sequential distribution potential point algorithm and polar angle distribution potential point algorithm.
      Conclusions  The results of this study prove the effectiveness and progressiveness of the proposed algorithm.

     

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