An Image Based Visual Servo Approach with Deep Learning for Robotic Manipulation
This addresses the problem of robotic manipulation for researchers in robotics and computer vision, representing an incremental improvement by applying deep learning to a known bottleneck.
This paper tackles the difficulty of extracting image features and estimating the Jacobian matrix in image-based visual servo by proposing a deep learning approach using a two-stream convolutional neural network to autonomously learn features and fit nonlinear relationships from image space to task space, achieving four degrees of freedom visual servo for robotic manipulation with experimental verification.
Aiming at the difficulty of extracting image features and estimating the Jacobian matrix in image based visual servo, this paper proposes an image based visual servo approach with deep learning. With the powerful learning capabilities of convolutional neural networks(CNN), autonomous learning to extract features from images and fitting the nonlinear relationships from image space to task space is achieved, which can greatly facilitate the image based visual servo procedure. Based on the above ideas a two-stream network based on convolutional neural network is designed and the corresponding control scheme is proposed to realize the four degrees of freedom visual servo of the robot manipulator. Collecting images of observed target under different pose parameters of the manipulator as training samples for CNN, the trained network can be used to estimate the nonlinear relationship from 2D image space to 3D Cartesian space. The two-stream network takes the current image and the desirable image as inputs and makes them equal to guide the manipulator to the desirable pose. The effectiveness of the approach is verified with experimental results.