In actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.

在实际的焊接场景中，需要有效的路径规划器来为周围有障碍物的焊接机械手在配置空间中找到无碰撞的路径。但是，作为一种最新方法，基于采样的计划器仅满足概率完整性，并且其计算复杂度对状态维很敏感。在本文中，我们提出了一种基于深度强化学习的焊接机械手路径规划器，以解决高维连续状态和动作空间中的路径规划问题。与基于采样的方法相比，它具有更强的鲁棒性，并且对状态维的敏感性较低。详细来说，为了提高学习效率，我们引入了逆运动学模块来提供先验知识，同时还设计了增益模块来避免局部最优策略，我们将它们集成到训练算法中。为了在多个维度上评估我们提出的规划算法，我们对焊接机械手进行了多组路径规划实验。结果表明，与大多数其他规划算法相比，我们的方法不仅提高了收敛性能，而且在规划的最优性和鲁棒性方面也更胜一筹。