用强化学习与运动基元实现多机械臂协同避障实时规划
Collaborative motion planning for multi-manipulator systems through Reinforcement Learning and Dynamic Movement Primitives
- 分层融合强化学习与动态运动基元，实现自适应轨迹生成
- 在PyBullet中使用UR5e机械臂验证，成功避免碰撞并实现实时规划
- 适合需要多臂协作的工业自动化场景，如装配与搬运
机器人任务常需多个机械臂协同以提升效率与速度，但会带来协作复杂性、避碰难题以及状态-动作空间扩大的挑战。为此，我们提出一种分层方法，结合强化学习（RL）与动态运动基元（DMP），利用示范库在动态环境中为新任务生成适应性强、实时的轨迹。该方法确保无碰撞轨迹生成与高效协同运动规划。通过在PyBullet仿真环境中的实验，使用UR5e机械臂对方法进行了验证。
原文摘要 · Abstract (English)
Robotic tasks often require multiple manipulators to enhance task efficiency and speed, but this increases complexity in terms of collaboration, collision avoidance, and the expanded state-action space. To address these challenges, we propose a multi-level approach combining Reinforcement Learning (RL) and Dynamic Movement Primitives (DMP) to generate adaptive, real-time trajectories for new tasks in dynamic environments using a demonstration library. This method ensures collision-free trajectory generation and efficient collaborative motion planning. We validate the approach through experiments in the PyBullet simulation environment with UR5e robotic manipulators.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。