面向电子商务仓储的深度强化学习物流车导航技术

Indoor Logistics Vehicle Navigation Technology Based on Deep Reinforcement Learning for E-Commerce Warehousing

摘要: 在各个领域逐渐智能化的情况下，电商经济也正在向智能化过渡，机器人导航技术的持续进步及其与物流运输的深度融合，正逐步推动室内物流运输向智能化方向发展。然而，当前大多数室内物流小车采用的基于同时定位与建图(SLAM)技术的导航方法存在一定的局限性。这类方法依赖于地图先验知识，不仅需要投入大量的人工成本和高精度的传感器，而且在环境发生变化时，物流小车往往难以有效完成导航任务，甚至在特定环境下可能引发安全问题。针对这一问题，本文提出了一种新型的基于深度强化学习的导航模型。该方法在TD3 (Twin Delayed Deep Deterministic Policy Gradient)这一经典深度强化学习算法的基础上，构建了适用于智能物流小车的导航框架。通过在仿真环境中的训练，本方法实现了移动机器人在未知环境中的自主导航。实验结果表明，相较于传统导航算法，本方法在导航准确率上具有显著优势。

Abstract: In the context of increasing intelligence across various fields, the e-commerce economy is also transitioning towards intelligence. The continuous advancement of robot navigation technology and its deep integration with logistics transportation are gradually driving the direction of indoor logistics towards intelligence. However, most indoor logistics vehicles currently adopt navigation methods based on Simultaneous Localization and Mapping (SLAM) technology, which have certain limitations. These methods rely on map prior knowledge, requiring significant manual labor and high-precision sensors. Moreover, when the environment changes, logistics vehicles often struggle to effectively complete navigation tasks and may even cause safety issues in specific environments. In response to this problem, this paper proposes a new type of navigation model based on deep reinforcement learning. This method constructs a navigation framework suitable for intelligent logistics vehicles on the basis of the classic deep reinforcement learning algorithm TD3 (Twin Delayed Deep Deterministic Policy Gradient). Through training in a simulation environment, this approach has achieved autonomous navigation of mobile robots in unknown environments. Experimental results show that this method has a significant advantage in navigation accuracy compared to traditional navigation algorithms.