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无人驾驶地面车辆控制后的路径:一种基于强化学习的方法,具有实验验证.

Yuanda Wang, Jingyu Cao, Jia Sun

    IEEE transactions on neural networks and learning systems
    |September 20, 2023
    PubMed
    概括

    一个新的强化学习 (RL) 策略增强了无人驾驶地面车辆 (USV) 的路径跟踪. 这种先进的控制政策提高了自主导航系统的跟踪精度和稳定性.

    科学领域:

    • 机器人和控制系统 机器人和控制系统
    • 人工智能和机器学习

    背景情况:

    • 无人驾驶地面车辆 (USV) 需要复杂的控制来实现自主导航.
    • 传统的控制方法经常与USVs的复杂,非线性动态作斗争.

    研究的目的:

    • 开发一种基于强化学习 (RL) 的策略,用于控制后的强大的USV路径.
    • 通过修改深度决定性政策梯度 (DDPG) 算法来提高跟踪精度和控制器稳定性.

    主要方法:

    • 实施了RL战略,整合了指导和定向控制,用于直接状态到指挥映射.
    • 在DDPG算法中引入了双关键设计和集成补偿器.
    • 利用预训练的基于神经网络的USV模型来处理未知的非线性动态.

    主要成果:

    • 拟议的基于RL的控制器与传统的级联和标准的DDPG控制器相比,表现优越.
    • 通过模拟和真实海上实验验验证了自我学习和路径跟踪能力.
    • 在跟踪准确性和稳定性方面取得了显著的改进.

    结论:

    • 开发的RL策略为USV路径后的控制提供了有效的解决方案.
    • 集成先进的RL技术和预测性USV模型增强了自主导航能力.

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  • 该方法对需要精确的USV机动性的现实世界应用具有前景.