混合强化学习让机器人在复杂产线自动规划安全顺滑的运动轨迹。
Hybrid Robot Learning for Automatic Robot Motion Planning in Manufacturing
- 分层混合架构：任务空间与关节空间双智能体协同决策。
- 融合可达性、关节限制、可操作性与碰撞风险，确保轨迹可行性。
- 适用于人机协作或多机共存的复杂制造场景，实测有效。
工业机器人广泛应用于多样化制造环境，但如何使机器人在任务变化时自动规划轨迹仍具挑战性，尤其在与机器、人员或其他机器人共处的工作单元中更为复杂。本文提出一种多层次混合机器人运动规划方法，结合基于任务空间的强化学习示范学习（RL-LfD）智能体与基于关节空间的深度强化学习（DRL）智能体。高层智能体学习在两者间切换以生成可行且平滑的运动轨迹。可行性通过整合环境中的可达性、关节极限、可操作性及碰撞风险进行计算。由此得到的混合运动规划策略能生成满足任务约束的可行轨迹。该方法在仿真机器人场景及真实世界设置中均验证了有效性。
原文摘要 · Abstract (English)
Industrial robots are widely used in diverse manufacturing environments. Nonetheless, how to enable robots to automatically plan trajectories for changing tasks presents a considerable challenge. Further complexities arise when robots operate within work cells alongside machines, humans, or other robots. This paper introduces a multi-level hybrid robot motion planning method combining a task space Reinforcement Learning-based Learning from Demonstration (RL-LfD) agent and a joint-space based Deep Reinforcement Learning (DRL) based agent. A higher level agent learns to switch between the two agents to enable feasible and smooth motion. The feasibility is computed by incorporating reachability, joint limits, manipulability, and collision risks of the robot in the given environment. Therefore, the derived hybrid motion planning policy generates a feasible trajectory that adheres to task constraints. The effectiveness of the method is validated through sim ulated robotic scenarios and in a real-world setup.
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