The application of scene recognition in intelligent robots to forklift AGV equipment is of great significance in order to improve the automation and intelligence level of distribution centers. At present, using the camera to collect image information to obtain environmental information can break through the limitation of traditional guideway and positioning equipment, and is beneficial to the path planning and system expansion in the later stage of warehouse construction. Taking the forklift AGV equipment in the distribution center as the research object, this paper explores the scene recognition and path planning of forklift AGV equipment based on a deep convolution neural network. On the basis of the characteristics of the warehouse environment, a semantic segmentation network applied to the scene recognition of the warehouse environment is established, and a scene recognition method suitable for the warehouse environment is proposed, so that the equipment can use the deep learning method to learn the environment features and achieve accurate recognition in the large-scale environment, without adding environmental landmarks, which provides an effective convolution neural network model for the scene recognition of forklift AGV equipment in the warehouse environment. The activation function layer of the model is studied by using the activation function with better gradient performance. The results show that the performance of the H-Swish activation function is better than that of the ReLU function in recognition accuracy and computational complexity, and it can save costs as a calculation form of the mobile terminal.

As a new type of part-to-picker storage system, the double-deep multi-tier shuttle system has been developed rapidly in the e-commerce industry because of its high flexibility, large storage capacity, and robustness. The system consists of a multi-tier shuttle sub-system that controls horizontal movement and a lift sub-system that manages vertical movement. The combination of shuttles and lifts, instead of a stacker crane in conventional automated storage and retrieval system, undertakes inbound/outbound tasks. Because of the complex structure and numerous equipment of the system, task scheduling has become a major difficulty in the outbound operation of the double-deep multi-tier shuttle system. Figuring out methods to improve the overall efficiency of task scheduling operations is the focus of current system application enterprises. This paper introduces the task scheduling problem for the shuttle system. Inspired from workshop production scheduling problems, we minimize the total time of a batch of retrieval tasks as the objective function, applying the modified Simulated Annealing Algorithms (SAAs) to solve the task scheduling problem. In conclusion, we verified the proposed model and the algorithm efficiency, using case studies.