A Knowledge Transfer-Based Genetic Algorithm for Multi-Target Robotic Arm Control

Zheng Guo, Zhaoping Yu, Wenbin Pei, Chuan Wang, Yaqing Hou, Chenchen Zhang, Zexuan Zhu, Xianneng Li

Abstract: The ability to swiftly and precisely reach any user-specified target location is necessary for a robotic arm that can be used in real-world scenarios. To date, many evolutionary optimization algorithms have been used to design controllers for robotic arms. However, when designing a robotic arm to reach multiple targets, most existing methods need to evolve the control strategy from scratch for each target, rather than trying to reuse existing experience. Therefore, computational resources are repeatedly and meaninglessly consumed. To this end, this paper proposes a genetic algorithm based on knowledge transfer (GAKT) dedicated to reusing existing knowledge to optimize a new robotic arm control task. Specifically, the knowledge transfer process can be summarized into the following two steps. First, through sequential transfer, GAKT initializes the population with the help of a knowledge base constructed by a quality diversity algorithm. Second, underperforming individuals are encouraged to acquire knowledge from excellent individuals in the same generation during the optimization process. We tested the effectiveness of GAKT and investigated its average performance by selecting multiple target points in different dimensions. The results show that GAKT can find the most advantageous arrival strategy (that is, make the end of the manipulator the closest to the target) on most of the selected targets. Moreover, we conducted ablation experiments and demonstrated the effectiveness of the knowledge transfer processes.