基于深度强化学习的无人艇轨迹跟踪算法研究
Journal of Huazhong University of Science and Technology(Nature Science Edition)(2023)
Abstract
针对欠驱动水面无人艇(USV)轨迹跟踪控制问题,提出一种基于近端策略优化(PPO)的深度强化学习轨迹跟踪控制算法.为引导控制器网络的正确收敛,构建基于长短时记忆(LSTM)网络层的深度强化学习控制器,设计了相应的状态空间和收益函数.为增强控制器的鲁棒性,生成轨迹任务数据集来模拟复杂的任务环境,以此作为深度强化学习控制器的训练样本输入.仿真结果表明:所提出的算法能有效收敛,具备扰动环境下的精确跟踪控制能力,有较大的实际应用潜力.
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Key words
unmanned surface vehicle(USV),trajectory tracking,deep reinforcement learning(DRL),proximal policy optimization(PPO),trajectory task data set
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