机器人运动规划以避免障碍物使用强化学习
Linda-Sophie Schneider1, Junyan Peng2, Andreas Maier2
1Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany. linda-sophie.schneider@fau.de.
Scientific reports
|September 12, 2025
概括
这项研究引入了机器人手臂的新增强化学习框架,将人工潜力场与深度决定性政策梯度相结合. 该方法提高了在复杂环境中避开障碍物和运动规划,提高了安全性和效率.
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
- 机器学习 机器学习
背景情况:
- 工业/实验室环境中的机器人手臂面临着在混乱的3D空间中避开障碍的挑战.
- 传统的路径规划方法在复杂的环境中经常与局部最佳和效率作斗争.
研究的目的:
- 开发一种新的强化学习框架,以提高机器人手臂的障碍回避和运动规划.
- 解决传统人工潜力场 (APF) 方法的局限性,特别是局部最佳和在密集障碍场景中的性能.
主要方法:
- 一种混合方法,将修改的人工潜力场 (APF) 方法与深度决定性政策梯度 (DDPG) 算法相结合.
- 在连续环境中制定,以更好地代表现实世界的机器人手臂操作.
- 整合强化学习因素和一个定制的奖励机制与补偿期限.
主要成果:
- 拟议的框架成功地导航复杂的3D空间,优化终端效应器轨迹并确保全身碰撞避免.
- 证明了APF局部最佳问题的缓解,特别是在障碍物距离很近的环境中.
- 与基线模型 (例如,TD3) 相比,通过更少的步骤和更低的能源消耗实现了更安全,更有效的障碍回避.
结论:
- 新的强化学习框架显著改善了机器人手臂运动规划和避障能力.
- 该方法为复杂,杂乱的环境提供了强大的解决方案,在效率和安全方面优于现有的方法.
- 这项工作为推进实际机器人应用中的智能自动化提供了巨大的潜力.
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