基于改进麻雀搜索算法的AUV路径规划方法

AUV path planning method based on improved sparrow search algorithm

  • 摘要:
    目的 针对复杂水下环境中自主水下航行器(AUV)在的三维路径规划算法存在的规划效果不理想、路径搜索不稳定等问题,提出一种基于改进麻雀搜索算法的AUV路径规划方法。
    方法 推导评价区间响应的矢量分析方法,引入分段学习和量子计算机制,改进了经典麻雀搜索算法的更新公式,通过汤普森采样,更新种群数量。在复杂洋流环境中进行仿真测试。
    结果 测试结果表明,提出的改进方案算法的平均最大时间较改进前减小了49.88%,在极端突变的洋流环境下的失败率减少了10.6%。
    结论 表明该方法具有较强的全局搜索能力和寻优性能、算法收敛性能较好,具备高效的路径规划能力。研究成果对AUV以及其他领域的路径规划问题有借鉴意义。

     

    Abstract:
    Objective To address the issues of suboptimal planning results and unstable path searches in three-dimensional path planning algorithms for autonomous underwater vehicles (AUVs) in complex underwater environments, this paper proposes an innovative AUV path planning method based on an improved sparrow search algorithm.
    Method The innovations of the new algorithm comprise the derivation of a vector analysis method for evaluating interval responses, introduction of segmented learning and quantum computing mechanisms, improvement of the update formula for the classic sparrow search algorithm, and updating of population quantities through Thompson sampling. Simulation tests are then conducted in complex ocean current environments.
    Results The results show that the proposed improved algorithm reduces the average maximum time by 49.88% compared to the previous version, and reduces the failure rate by 10.6% in extreme and abrupt ocean current environments, demonstrating strong and efficient global search capability, optimization performance and path planning ability.
    Conclusion The proposed algorithm exhibits good convergence, making it suitable for underwater path planning in dynamic environments. The research findings have significant implications for path planning problems in AUVs and other domains.

     

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