Research on tracking strategy of manipulator based on fusion reward mechanism

Deep reinforcement learning algorithms are widely used in the field of robot control. Sparse reward signals lead to blind exploration, affecting the efficiency of the manipulator during path planning for multi-axis systems at any given end-effector start and target position. To address the problem of tracking randomly located targets in three-dimensional space, this paper proposes a PPO (proximal policy optimization) algorithm with a fused reward mechanism, which enhances the tracking and guidance capabilities of the manipulator in multiple dimensions and reduces the blind randomness of the manipulator during the detection and sampling process. The fusion reward mechanism consists of four dimensions: trajectory correction reward, core area acceleration guidance reward, ladder adaptability reward, and abnormal termination penalty. Finally, a 7-degree-of-freedom Kuka manipulator is built on the PyBullet platform for simulation experiments. Experimental results show that, compared with the sparse reward mechanism, the PPO algorithm with the fused reward mechanism has a higher average success rate as high as 94.88% in task tracking, which can effectively improve the tracking efficiency and accuracy of the spatial manipulator.