Abstract:

Continuing the research on multi-task balancing strategy for robot manipulators， the use of parallel robot manipulators in automated assembly lines allows for precise grasping and handling of dynamic objects on conveyors. However， the challenge lies in ensuring leakage-free handling of materials， which cannot be achieved by a single robot manipulator. This highlights the importance of multi-robot manipulator collaboration. In this study， a novel approach based on the deep Q-learning （DQN） algorithm is proposed to address the task balancing problem for robot manipulators. The objective is to minimize the cost function associated with the manipulation tasks. To improve the performance of the DQN algorithm， the study incorporates the actor-critic algorithm and introduces the concept of balancing advantage function. This allows for the optimization of the execution strategy， ensuring a balanced allocation of tasks among the robot manipulators. Experimental results validate the effectiveness of the proposed algorithm. It demonstrates a 17.1% improvement in convergence performance compared to the traditional DQN algorithm. Additionally， under predefined experimental conditions， the proposed algorithm achieves approximately a 31.6% reduction in overall cost when compared with the baseline algorithm. This significant reduction in cost enhances the overall execution efficiency of robot manipulators in multi-task scenarios.

Key words: robot manipulator, deep Q-learning algorithm, balanced allocation of task, multi-task