通过将DRL与噪声层和MPC集成来规划无碰撞机器人路径
Xinzhan Hong1,2, Qieshi Zhang3, Yexing Yang1
1Hangzhou Institute of Technology, Xidian University, Hangzhou 311231, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
本研究介绍了自主移动机器人 (AMR) 的混合路径规划框架,该框架将优化的深度强化学习 (DRL) 与模型预测控制 (MPC) 结合起来. 这种新的方法提高了在动态的工业环境中避开障碍和提高了导航效率.
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
- 控制系统 控制系统
背景情况:
- 自主移动机器人 (AMR) 对工业自动化和物流至关重要.
- 在动态环境中的路线规划在平衡效率,响应性和安全性方面提出了挑战.
- 现有的模型预测控制 (MPC) 和深度强化学习 (DRL) 方法具有个别的局限性.
研究的目的:
- 开发一个混合路径规划框架,将优化的DRL算法与MPC集成在一起.
- 增强AMR在复杂,动态环境中的避障和导航能力.
- 克服独立DRL和MPC方法的局限性.
主要方法:
- 提出了一个混合框架,结合了优化的DRL算法 (TD3具有可学习的噪音线性层) 和MPC.
- 对于一个短视野的参考轨迹,DRL会生成逐步控制命令.
- MPC使用限制意识优化来改进轨迹,以避免障碍.
主要成果:
- 混合方法显著提高了避开障碍的成功率.
- 与传统方法相比,证明了更好的轨迹流性和路径准确性.
- 在各种动态环境中实现了强大而高效的自主导航.
结论:
- 集成的DRL-MPC框架为复杂的自主导航提供了强大的解决方案.
- 这种方法有效地将DRL的适应性与MPC的可靠性相结合.
- 拟议的方法在动态的工业环境中为AMR路径规划提供了显著的改进.
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