Zhe Liu
ABSTRACT
The use of visual systems to judge the surrounding environment and locate work targets is a key link for efficient and reliable completion of live working tasks by substation live working robots. The traditional feature object detection algorithms used by live working robots in the past cannot meet the recognition and positioning accuracy requirements of live working in complex working environments of substations. Therefore, this article proposes a visual positioning method for substation live working robots based on deep learning models. It uses 10000 annotated complex substation scene photos containing various types of equipment as the training set, trains the visual positioning method proposed in this article, tests 2000 photos of complex substation scenes containing various equipment, and analyzes the recognition and positioning results.
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Index Terms
- Research on Visual Localization Method for Substation Live Working Robot Based on Deep Learning
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