一种PSO增强的高斯伪光谱方法,用于解决自动水下车辆的轨迹规划
Wenyang Gan1, Lixia Su1, Zhenzhong Chu1,2
1Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
本研究介绍了一种用于自动水下车辆 (AUV) 导航的快速优化方法. 将粒子群优化 (PSO) 和高斯伪光谱法 (GPM) 结合起来,可显著加快路径规划以避免障碍.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 计算数学 计算数学 计算数学
背景情况:
- 自主水下车辆 (AUV) 需要高效的轨道优化,以确保安全的航行.
- 现有的方法在优化算法中经常面临初始值依赖的挑战.
- 避开障碍是AUV任务规划的一个关键方面.
研究的目的:
- 开发AUV避障轨迹规划的快速优化方法.
- 为了解决顺序二次编程 (SQP) 算法的初始值灵敏度.
- 为了提高轨迹优化的计算效率.
主要方法:
- 建立了一个多约束轨迹规划模型,结合了动态,边界和路径约束.
- 用高斯伪光谱方法 (GPM) 将轨迹优化转换为非线性编程 (NLP) 问题.
- 建议采用混合方法,将粒子优化 (PSO) 与GPM和SQP相结合,用于预先规划.
主要成果:
- 拟议的PSO-GPM方法有效地为SQP算法提供了合适的初始值,加速了融合.
- 计算效率得到了显著的提高:比线性插值提高了82.3%,比立方线插值提高了88.6%.
- 模拟分析证实了PSO-GPM方法在优化AUV轨迹方面的有效性和速度.
结论:
- 将PSO预先规划与GPM集成,为AUV轨迹优化提供了一个计算效率高的解决方案.
- 这种方法克服了SQP中的初始价值依赖问题,导致更快的融合.
- 该PSO-GPM方法被验证为复杂的AUV导航任务的有效策略.
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