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动态机器人跟踪水下目标,使用强化学习

I Masmitja^1,2, M Martin^3,4, T O'Reilly²

¹Institut de Ciències del Mar (ICM), CSIC, Barcelona 95062, Spain.

Science robotics

|July 26, 2023

概括

深度强化学习优化了自主水下机器人路径规划,用于追踪海洋动物. 这种进步提高了海洋观测能力,有助于了解深海种群和保护策略.

科学领域:

海洋机器人海洋机器人
深海生态深海生态
海洋学中的人工智能

背景情况:

自主水下机器人提供可扩展的海洋观测,但需要先进的技术来完成复杂的任务.
追踪移动的水下目标对于研究海洋动物连接和深海种群分布至关重要,这代表了重要的知识差距.
目前用于在动态水下环境中放置传感器和规划路径的方法具有挑战性.

研究的目的:

研究强化学习的应用,以优化水下目标追踪的距离.
评估强化学习作为自主地表车辆跟踪移动水下目标的路径规划系统的有效性.

主要方法:

实施强化学习方法作为自主地表车辆的路径规划系统.
使用开源模型进行评估,模拟环境中的性能指标,以及超过15小时的海上实地实验.

主要成果:

证明了深度强化学习的成功应用,用于自主水下目标追踪.
通过模拟和广泛的现实世界海洋实验验证实了这一方法.

结论:

深度强化学习是一种强大的工具,可以提高在海洋环境中的自主机器人的能力.
这种方法鼓励部署先进的算法,以有效监测海洋生物系统.

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