A robot vision navigation method using deep learning in edge computing environment

Abstract

Abstract In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in the operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses a cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by a vision system. Then, it extracts the route of processed images based on the improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments and has good practicability and applicability.

Keywords

• Edge computing
• Agricultural robot
• Cascaded deep convolutional network
• Hybrid dilated convolutional network
• Improved Hough transform
• Farmland image segmentation