基于机器视觉的工业机械臂自动抓取系统在电商物流中的应用研究

随着电子商务的蓬勃发展,物流自动化特别是高效准确的物品分拣和抓取技术变得至关重要。针对传统机械臂在动态环境中适应性不足的问题,本文提出了一种基于机器视觉的工业机械臂自动抓取系统。该系统利用深度学习技术,通过实时处理图像数据,精确提取物品特征,并计算6D姿态信息,以指导机械臂执行准确的抓取动作。本研究的核心是一个深度神经网络,它结合了深度可分离卷积和U型网络的优势,显著提高了特征提取的效率和准确性,同时减少了模型的参数量,增强了系统的实时性。在Cornell抓取数据集上的实验结果表明,该系统达到了98.79%的准确率,证明了其在电商物流自动化领域的应用潜力。此外,系统还采用了高斯滤波和数据增强技术,进一步提升了模型的稳定性和泛化能力。对比实验显示,与其他现有技术相比,本系统在保持高准确率的同时,模型参数量更少,更适合实时应用。With the booming development of e-commerce, logistics automation, especially efficient and accurate item sorting and grasping technology, has become crucial. Aiming at the problem of the lack of adaptability of traditional robot arm in dynamic environment, an automatic grasping system of industrial robot arm based on machine vision is proposed in this paper. The system uses deep learning technology to accurately extract item features by processing image data in real time, and calculates 6D pose information to guide the robot arm to perform accurate grasping actions. The core of this research is a deep neural network, which combines the advantages of deep separable Convolution and U-Net to significantly improve the efficiency and accuracy of feature extraction, while reducing the number of parameters in the model and enhancing the real-time performance of the system. The experimental results on the Cornell crawl data set show that the system achieves 98.79% accuracy, which proves its application potential in the field of e-commerce logistics automation. In addition, the system also adopts Gaussian filtering and data enhancement technology, which further improves the stability and generalization ability of the model. Comparative experiments show that compared with other prior art, the system has fewer model parameters while maintaining high accuracy, and is more suitable for real-time applications.

面向电子商务仓储的深度强化学习物流车导航技术

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。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.

融合知识图谱与图注意力的电子商务推荐算法

随着大数据时代的发展,电子商务进入了新的发展阶段,推荐系统已成为提升用户体验和促进销售的关键技术。本文提出一种融合知识图谱(KG)与图注意力(GAT)的电子商务推荐算法(E-KGAT)。首先,将用户行为数据与商品属性相结合构建协同知识图谱并进行嵌入表示。然后,设计了一个基于注意力机制的嵌入传播层,捕获节点间的相互作用与依赖关系。此外,通过层聚合机制整合各层的节点表示,预测用户对商品的匹配得分,从而完成推荐任务。最后,采用BCE损失函数和Adam优化器优化模型性能,确保模型在训练过程中准确学习用户和商品的嵌入表示。实验结果表明,通过与其他算法结果比较和消融实验结果比较,证明本算法相比典型的推荐算法不仅能更深入理解用户偏好,还能准确捕获商品之间的关系,从而实现更高的准确性和可解释性。With the development of the big data era, e-commerce has entered a new stage of development, and recommender system has become a key technology to enhance user experience and promote sales. This paper proposes an e-commerce recommendation algorithm (E-KGAT) that integrates knowledge graph (KG) and graph attention (GAT). First, user behavior data and product attributes are combined to construct a collaborative knowledge graph and embedded representation. Then, an embedding propagation layer based on the attention mechanism is designed to capture the interactions and dependencies among nodes. In addition, the node representations of each layer are integrated through the layer aggregation mechanism to predict the user’s matching score for commodities, thus accomplishing the recommendation task. Finally, the binary cross-entropy loss function and Adam optimizer are used to optimize the model performance and ensure that the model accurately learns the embedded representations of users and commodities during training. The experimental results show that by comparing with other algorithms and comparing with the results of ablation experiments, it is proved that the present algorithm not only understands the user preferences more deeply than typical recommendation algorithms, but also accurately captures the complex relationship between commodities, thus realizing higher accuracy and interpretability.