Abstract
New study results on robotics have been published. According to news originating from Hubei, People's Republic of China, by NewsRx correspondents, research stated, “IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues.” The news correspondents obtained a quote from the research from Hanjiang Normal University: “Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention. MethodsTo fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups. ResultsThrough extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.
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NSTL
Frontiers Media Sa