A welding manipulator path planning method combining reinforcement learning and intelligent optimisation algorithm. (18th March 2020)

• Record Type: Journal Article
• Authors: Zhang, Junhua; Cheng, Lianglun; Wang, Tao; Xia, Wenya; Yan, Dejun; Wu, Zhiheng; Duan, Xianyun
• Is Part Of: International journal of modelling, identification and control. Volume 33:Number 3(2020)
• Page Start: 261
• Page End: 270
• Publication Date: 2020-03-18
• Subjects: complex component -- welding manipulator -- path planning -- direction constraint -- deep deterministic policy gradient -- DDPG -- adaptive ant colony optimisation -- AACO; Engineering -- Methodology -- Periodicals; Science -- Methodology -- Periodicals; 001.42
• Languages: English
• ISSNs: 1746-6172

Abstract

We present DDPG-AACO, a hierarchical method for welding manipulator path planning of complex components welding tasks that combines reinforcement learning (RL) with the intelligent optimisation algorithm. The RL agent, trained with the deep deterministic policy gradient (DDPG), learns local path planning policies that control the welding manipulator to safely move between two welding seam endpoints. Next, on a distance matrix constructed by the lengths of local paths between every two welding seam endpoints, the adaptive ant colony optimisation (AACO) algorithm with artificially changed value of pheromones is adopted to realise the global path planning that the welding manipulator traverses all welding seams under a welding direction constraint and has the shortest path length. The simulation results show the effectiveness of the method. The DDPG is better than the deep Q-learning-based methods when performing local path planning. Moreover, the length of the global path with direction constraint can converge to the minimum.