Abstract
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.
Highlights
In the application of forklift AGV equipment path planning in the distribution center, the main tasks include road scene recognition, path planning, obstacle identification, and local obstacle avoidance, etc
The road scene recognition of forklift AGV equipment is a very important task, especially the use of pure visual methods, as these problems have high complexity and are more challenging when applied to the distribution center warehouse environment
In order to realize the convolution neural network established in this paper, it was used to identify the road scene of forklift AGV equipment in a distribution center warehouse, using the PyTorch deep learning framework, which is the most popular in the field of deep learning and is very suitable for image data processing
Summary
In the application of forklift AGV equipment path planning in the distribution center, the main tasks include road scene recognition, path planning, obstacle identification, and local obstacle avoidance, etc. In the aspect of scene recognition and path planning, a large number of scholars have studied and made great achievements in SLAM (simultaneous localization and mapping) [1,2], deep learning [3,4,5,6,7], and other aspects These methods are often aimed at the general indoor and outdoor life scenes, but the adaptability of AGV equipment for forklift trucks in logistics and distribution centers is low. The use of the deep learning method for scene recognition, which is different from the SLAM method and target detection, can obtain more abundant image feature information, including image semantic information, texture information, and local features.
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