基于强化学习的干扰下欠驱动无人水面车辆预测避障算法

IF 5.2 2区 计算机科学 Q2 ROBOTICS
Kefan Jin, Zhe Liu, Jian Wang
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引用次数: 0

摘要

由于各种海上任务的日益复杂,欠驱动无人水面航行器(USV)已成为研究热点。深度强化学习(DRL)技术的快速发展为无人潜船的自主控制提供了一种新的方法,使得对目标无人潜船的动态建模变得不必要。为了进一步提高无人潜航器对海上干扰的避碰性能,本文提出了一种预测强化学习的无人潜航器避障控制方法。设计了一个预测模块来生成描述环境状态的潜在特征。然后,为基于drl的策略模块提供预测特征,生成欠驱动无人水面车辆的动作分布。本文提出的方法可以使无人潜航器不依赖全局先验信息,仅依靠其局部观测信息就能避开障碍物并到达目的地。仿真和物理实验表明,与一般DRL方法相比,该方法对环境干扰具有更强的鲁棒性,使无人潜航器能够在避开障碍物的同时到达目的地。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Predictive Obstacle Avoidance Algorithm for Under-Actuated Unmanned Surface Vehicle Under Disturbances via Reinforcement Learning

Predictive Obstacle Avoidance Algorithm for Under-Actuated Unmanned Surface Vehicle Under Disturbances via Reinforcement Learning

Due to the growing complexity of diverse maritime tasks, underactuated unmanned surface vehicle (USV) has become a research hotspot. The rapid development of deep reinforcement learning (DRL) technology has brought forth a novel approach for the USV autonomous control, rendering unnecessary the dynamical modeling of the target USV. To further improve the USV collision avoidance performance against maritime disturbances, this paper presents a predictive reinforcement learning method for USV obstacle avoidance control. A prediction module is designed to generate latent features that depict environmental states. After that, the prediction feature is provided for a DRL-based policy module to produce an action distribution for the underactuated unmanned surface vehicle. The proposed method in this paper can enable the USV avoid obstacle and reach the destination solely based on its local observational information, without relying on prior global information. Simulation and physical experiments have demonstrated that, compared to general DRL methods, the proposed method exhibits stronger robustness to environmental disturbances, enabling the USV to reach the destination while avoid the obstacle.

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来源期刊
Journal of Field Robotics
Journal of Field Robotics 工程技术-机器人学
CiteScore
15.00
自引率
3.60%
发文量
80
审稿时长
6 months
期刊介绍: The Journal of Field Robotics seeks to promote scholarly publications dealing with the fundamentals of robotics in unstructured and dynamic environments. The Journal focuses on experimental robotics and encourages publication of work that has both theoretical and practical significance.
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