通过半监督的增量广义学习,通过基于轴承刚性的AUV集群控制
概括
本研究介绍了使用图形理论和机器学习的自动水下车辆 (AUV) 的新型集群控制. 这些方法提高了定位准确性,并减少了在具有挑战性的海洋环境中训练时间.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 海洋工程 海洋工程
- 控制系统 控制系统
背景情况:
- 集群控制对于自主水下车辆 (AUV) 在海洋任务中的协调至关重要.
- 现有的方法在通信薄弱和复杂的环境中面临挑战.
- 在这些条件下,AUV的强有力的集群策略存在研究缺口.
研究的目的:
- 解决AUV集群控制在弱通信和复杂的海洋环境中的研究缺口.
- 为AUV开发一个强大而高效的集群策略.
- 为了提高AUV协调中的定位精度和能源效率.
主要方法:
- 利用图形理论,特别是轴承刚度图,以建模AUV拓.
- 开发了一个代梯度下降基于本地化估计器.
- 实施了最小权重轴承刚度图的策略,以改善本地化和能源效率.
- 在充满障碍的环境中使用半监督的广泛学习系统 (BLS) 用于无模型的集群控制器.
主要成果:
- 最低权重轴承刚度图的策略有效地平衡了定位精度和通信消耗.
- 与监督方法相比,半监督的BLS控制器减少了培训时间,克服了标签限制.
- 模拟和实验研究验证了拟议的集群控制策略.
结论:
- 拟议的最小加权轴承刚度图局部化策略在准确性和通信成本之间提供了卓越的平衡.
- 基于BLS的半监督集群控制器为AUV在复杂环境中提供了高效的解决方案,具有有限的标记数据.
- 综合方法提高了AUV复杂的海洋协调任务的可行性.
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