2023/12/11 by Fangqing Chen, Chen, Fangqing
Computer Science · Engineering · #Advanced Vision and Imaging #FOS: Computer and information sciences #Image Processing Techniques and Applications #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2312.06340
openalex publication_date 2023/12/11 · openalex created_date 2023/12/14 · openalex updated_date 2026/07/28
This paper introduces a manipulation framework for the elastic rod, including shape representation, sensorimotor-model estimation, and shape controller. Until now, the manipulation of the elastic rod has faced several challenges: 1) shape learning from high-dimensional to low-space dimensional; 2) the modeling of robot manipulation of the elastic rod; 3) the determination of the shape controller. A novel manipulation framework for the elastic rod is presented in this paper, which only uses the input and output data of the system without any prior knowledge of the robot, camera, and object. The proposed approach runs in a model-free manner. For the approximation of the sensorimotor model, adaptive Kalman filtering (AKF) is used as the online estimation. Model-free adaptive control (MFAC) is designed according to the obtained differential model of robot-object configuration and then is combined with the performance regulation requirement to give the final format of the shape controller. Hence, the proposed approach can enhance the autonomous capability of deformation object manipulation. Detailed simulation results are conducted with a single robot manipulation to evaluate the effectiveness of the proposed manipulation framework.