多AUV动态任务分配基于自组织地图神经网络和Dubins路径生成器
Xin Li1, Wenyang Gan2, Wen Pang3
1College of Information Engineering, Shanghai Maritime University, 1550 Haigang Avenue, Pudong New Area, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究介绍了一种改进的任务分配算法,用于具有动力限制的多无人水下车辆 (多AUV) 系统. 这种新的方法确保了成功的路径规划和任务完成,即使有转向限制.
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
- 控制系统 控制系统
背景情况:
- 由于动力学约束,多AUV系统在任务分配方面面临挑战.
- 未经更新的AUV需要复杂的算法才能高效运行.
- 由于障碍物或车辆限制,可能会出现轨道规划失败.
研究的目的:
- 为多AUV系统提出一个改进的任务分配算法.
- 为了解决动力学约束和潜在的轨迹规划失败.
- 确保AUV在复杂环境中成功完成任务.
主要方法:
- 结合了改进的自我组织地图 (SOM) 神经网络与杜宾斯路径算法.
- 使用工作负载平衡和邻里函数进行初始任务分配.
- 当动力学约束或障碍妨碍路径规划时,通过调整SOM神经重量来实现动态任务重新分配.
主要成果:
- 该算法有效地将任务分配给AUV,考虑工作负载平衡.
- 动态重新分配确保在动力学约束和障碍下路径可行性.
- 模拟结果表明多AUV系统的路径规划和任务完成成功.
结论:
- 拟议的算法提高了多AUV系统的任务分配效率.
- 它为在动力学约束下运行的AUV提供了强大的解决方案.
- 集成SOM和Dubins Path算法为复杂的AUV任务提供了一种可行的方法.
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