Image-Based Visual Servoing Control of Robot Manipulators Using Hybrid Algorithm with Feature Constraints

Release time:2021-09-08  Hits:

• Affiliation of Author(s):电气与电子工程学院
• Journal:IEEE ACCESS
• Funded by:国家自然科学基金项目
• Key Words:Bidirectional extreme learning machine, Smooth variable structure filter, Constraint function, Restricted region, Visual servoing
• Abstract:The challenge in addressing uncalibrated visual servoing (VS) control of robot manipulators with unstructured environments is to obtain appropriate interaction matrix and keep the image features in the field of view (FOV), especially when the non-Gaussian noise disturbance exists in the VS process. In this paper, a hybrid control algorithm which combines bidirectional extreme learning machine (B-ELM) with smooth variable structure filter (SVSF) is proposed to estimate interaction matrix and tackle visibility constraints. For VS, the nonlinear mapping between image features and interaction matrix is approximated using the B-ELM learning. To increase the capability of anti-interference, the SVSF is employed to reestimate interaction matrix. A constraint function presenting feature coordinates and region boundaries is given and added to the velocity controller, which drags image features away from the restricted region and ensures the smoothness of the velocities. Since the camera and robot model parameters are not required in developing the control strategy, the servoing task can be fulfilled flexibly and simply. Simulation and experimental results on a conventional 6-degree-of-freedom manipulator verify the effectiveness of the proposed method.
• Co-author:李洪文,任晓琳
• First Author:renxiaolin
• Indexed by:Journal paper
• Volume:8
• Page Number:1
• Number of Words:2
• Translation or Not:no
• Date of Publication:2020-12-28