在三维地形和海洋环境中使用混合粒子群和动态窗口算法进行多AUV动态合作路径规划
1Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Biomimetics (Basel, Switzerland)
|August 27, 2025
概括
这项研究介绍了一种混合算法,用于在复杂的海洋环境中导航的多个自主水下车辆 (AUV). 这种新方法提高了路径规划效率和安全性,减少了路径长度和碰撞威胁.
科学领域:
- 机器人技术
- 海洋工程
- 人工智能
背景情况:
- 在复杂的3D水下地形和动态的海洋流中, 多个自主水下车辆 (AUV) 的合作路径规划具有挑战性.
- 传统的粒子群优化 (PSO) 算法与局部优化,粒子分布不均,以及在动态海洋环境中的适应性不佳作斗争.
研究的目的:
- 提出一个混合算法,将改进的多目标粒子群优化 (IMOPSO) 和动态窗口算法 (DWA) 结合起来,用于强大的多AUV合作路径规划.
- 在复杂,动态的水下环境中解决传统公共服务的局限性,提高路径规划效率和安全性.
主要方法:
- 开发了一种混合初始化方法 (CPB),使用切比什夫混乱映射,前代消除和边界粒子注入.
- 改进的PSO具有动态参数调整和混合扰动机制.
- 集成的DWA用于实时动态避障和路径校正,创建全球和本地合的框架.
- 采用立方线插曲来平滑路径,并使用动态惩罚功能来避免碰撞和地形限制.
主要成果:
- 拟议的IMOPSO-DWA算法有效地规划了多个AUV在具有挑战性的环境中的动态安全路径.
- 与传统算法相比,模拟结果显示了更高的效率和安全性.
- 与标准PSO相比,混合算法减少了15.5%的路径长度,8.3%的威胁惩罚,以及3.2%的总体适应性.
结论:
- IMOPSO-DWA混合算法为多个AUV在动态水下环境中的合作路径规划提供了卓越的解决方案.
- 该算法通过有效平衡路径长度,平滑度,偏移角度和能量损耗来满足导航要求.
- 这种方法在复杂的海洋环境中提高了AUV导航的安全性和效率.
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