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基于改进的群优化,基于AUV的3D平滑路径规划,考虑到方向切换压力
Ronghua Meng1,2,3, Aiwen Sun2,3,4, Zhengjia Wu5,6
1Hubei Key Laboratory of Construction and Management in Hydropower Engineering, China Three Gorges University, Yichang, 443002, Hubei, China.
Scientific reports
|July 31, 2023
概括
本研究介绍了一种用于自动水下车辆的新型路径规划算法,通过减少航向变化和优化路径平滑度来提高复杂海洋环境中的安全性和效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 海洋工程 海洋工程
背景情况:
- 自主水下车辆 (AUV) 在复杂的海洋环境中面临挑战,包括沉重的转向切换压力.
- 传统的空间路径规划方法受到低维搜索域和不准确的空间通信信息的影响.
研究的目的:
- 为AUV开发一个流和安全的空间路径规划算法.
- 为了解决传统方法的局限性,并减少转向切换压力.
主要方法:
- 整合了改进的群优化 (ACO) 与纠正连接的空间搜索策略.
- 基于网格环境模型的空间连接相邻域搜索策略的设计.
- 为ACO引入启发式函数和费洛蒙更新标准,以提高路径流性.
主要成果:
- 拟议的太空搜索战略提高了安全路径规划的成功概率,而不会减少可探索的自由空间.
- 改进的ACO算法保证了最短的路径,最小的曲率和最少的转时间.
- 模拟证实了集成算法在复杂的海洋环境中的有效性.
结论:
- 新的路径规划算法有效地降低了AUV的方向切换压力.
- 该算法在复杂的水下环境中确保了流,安全和优化的路径.
- 这种方法代表了AUV导航和空间规划的重大进步.
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