Wang Lei, Liu Guangjun
School of Economy and Management, Hanjiang Normal University, Shiyan, Hubei, China.
School of Business, Wuchang University of Technology, Wuhan, Hubei, China.
Front Neurorobot. 2024 Jan 23;17:1329589. doi: 10.3389/fnbot.2023.1329589. eCollection 2023.
In the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. 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.
To 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.
Through 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.
In addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.
在物流仓储机器人领域，协同操作与协调控制一直是具有挑战性的问题。尽管深度学习和强化学习方法在解决这些问题方面取得了一些进展，然而，当前的研究仍存在不足。特别是，对多机器人集群的自适应感知和实时决策的研究尚未得到足够的关注。
为了填补这一研究空白，我们提出了一种基于A3C优化的YOLOv5-PPO模型。该模型巧妙地结合了YOLOv5的目标检测能力和PPO强化学习算法，旨在提高物流仓储机器人组之间协同操作的效率和准确性。
通过在多个数据集和任务上进行广泛的实验评估，结果表明，在不同场景下，我们的模型能够成功实现多机器人协同操作，显著提高任务完成效率，并保持目标检测和环境理解的高精度。
此外，我们的模型表现出优异的鲁棒性和适应性，能够适应环境的动态变化和需求的波动，为解决物流仓储机器人的协同操作问题提供了一种有效的方法。
