所属单位：电气与电子工程学院
发表刊物：IEEE ACCESS
项目来源：国家自然科学基金项目
关键字：Bidirectional extreme learning machine, Smooth variable structure filter, Constraint function, Restricted region, Visual servoing
摘要：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.
合写作者：李洪文,任晓琳
第一作者：任晓琳
论文类型：期刊论文
卷号：8
页面范围：1
字数：2
是否译文：否
发表时间：2020-12-28