Reinforcement Learning-Based Simulation of Seal EngravingRobot in the Context of Artificial Intelligence  

作　　者：Ran Tan Khayril Anwar Bin Khairudin 

机构地区：[1]College of Creative Art,Universiti Teknologi MARA Cawangan Perak,Kampus Seri Iskandar,Malaysia

出　　处：《Journal of Artificial Intelligence and Technology》2024年第4期288-295,共8页人工智能技术学报（英文）

摘　　要：The rapid development of robotics technology has made people’s lives and work more convenient and efficient.Theresearch and simulation of robots combined with reinforcement learning intelligent algorithms have become a hotspot in variousfields of robot applications.In view of this,this study is based on deep reinforcement learning convolutional neural networks,combined with point cloud models,proximal strategy optimization algorithms,and flexible action evaluation algorithms.A sealcutting robot based on deep reinforcement learning has been proposed.The final results show that the descent speed of the sealcutting robot with the root mean square difference as the performance standard is about 1%faster than the flexible actionevaluation algorithm.About 2%is faster than the proximal strategy optimization algorithm.It is about 4%faster than the deepdeterministic strategy gradient algorithm.This indicates that the research model has certain advantages in terms of actualaccuracy after cutting.The fluctuation of this model is about 10%smaller than the evaluation of flexible actions and about 60%smaller than the gradient of deep deterministic strategies.Therefore,the research model has the highest overall stability withoutfalling into local optima.In addition,compared to the near-end strategy optimization algorithm,it falls into local optima,resultingin a low coincidence degree of about 17%.The deep deterministic strategy gradient algorithm has a large fluctuation amplitudeduring the seal cutting process,and the overall curve is relatively slow,with a final overlap of about 70%.The overlap degree offlexible action evaluation is slightly higher by about 83%.The maximum stability of the model’s overlap is best around 90%.Through experiments,it can be found that the seal cutting robot proposed in the study based on deep reinforcement learningmaintains certain advantages in performance indicators in various types of tests.

关 键 词：flexible action evaluation point cloud model reinforcement learning ROBOTS SIMULATION 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP18[自动化与计算机技术—控制科学与工程]