DPDQN-TER:在动态场景中改进了移动机器人路径规划的深度强化学习方法
Shuyuan Gao1, Yang Xu1, Xiaoxiao Guo2
1Mechanical and Rail Transit, Changzhou University, Changzhou 213164, China.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
本研究介绍了DPDQN-TER,这是一种用于复杂环境中移动机器人路径规划的新方法. 它通过基于变压器的序列建模和多分支政策网络来提高学习效率和稳定性.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 有效的路径规划对于移动机器人在动态,障碍密集的工业环境中至关重要.
- 传统的强化学习在复杂场景中与经验利用和政策代表性作斗争.
研究的目的:
- 为移动机器人在动态环境中开发先进的路径规划方法.
- 提高学习效率,政策稳定性和导航性能.
主要方法:
- 集成基于变压器的序列建模与多分支参数策略网络.
- 实施一个具有时间意识的体验重复机制与多头自我注意.
- 动态权重和阻碍回避经验的抽样.
主要成果:
- 与基准算法相比,DPDQN-TER显示出更高的成功率,更短的路径长度和更快的融合.
- 废弃研究证实了变压器增强重复和多部门政策网络的重大贡献.
- 该方法在静态和动态环境中显示出更好的性能和概括能力.
结论:
- 在复杂的工业测量场景中,DPDQN-TER为自主导航提供了强大的解决方案.
- 拟议的方法提高了移动机器人的实用性和环境适应性.
- 变压器模型和多部门政策的整合促进了机器人技术的强化学习.
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