Robot target positioning based on residual BP neural network
In this article,in order to ensure the target positioning of the monocular vision robot with unknown in-depth infor-mation,the three-dimensional coordinate self-decouple target-positioning method is proposed based on the three residual neural networks,with the help of the camera's imaging model.Firstly,YOLOv5s is subject to the lightweight improvement,and thus the target is positioned initially.Then,OpenCV is used to extract the target's size features.Next,they are fused with the camera's imaging model,so as to derive the functional relationship between the world coordinate system,the pixel coordinate system,and the size features.Finally,since the residual network is beneficial to avoid gradient disappearance,the three residual BP neural networks are used to map the coordinate-transformation function,which reduces the workload of a single neural network and ob-tains the information on the target's three-dimensional coordinate.The results show that the highest positioning error of this meth-od is 0.747％.
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