Novel Joint-Drift-Free Scheme at Acceleration Level for Robotic Redundancy Resolution With Tracking Error Theoretically Eliminated

• Published in: IEEE/ASME transactions on mechatronics Vol. 26; no. 1; pp. 90 - 101
• Main Authors: Jin, Long, Xie, Zhengtai, Liu, Mei, Chen, Ke, Li, Chunxu, Yang, Chenguang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Acceleration-level joint-drift-free (ALJDF) scheme; Cartesian coordinates; Control stability; Drift; dynamic neural network (DNN); Dynamic stability; dynamics level; Error analysis; Indexes; Kinematics; Manipulator dynamics; Manipulators; Neural networks; Optimization; Performance assessment; Redundancy; redundant manipulator; Robot arms; Robot control; Stability analysis; Task analysis; Tracking errors; Velocity errors
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2020.3001624

In this article, three acceleration-level joint-drift-free (ALJDF) schemes for kinematic control of redundant manipulators are proposed and analyzed from perspectives of dynamics and kinematics with the corresponding tracking error analyses. First, the existing ALJDF schemes for kinematic control of redundant manipulators are systematized into a generalized acceleration-level joint-drift-free scheme with a paradox pointing out the theoretical existence of the velocity error related to joint drift. Second, to remedy the deficiency of the existing solutions, a novel acceleration-level joint-drift-free (NALJDF) scheme is proposed to decouple Cartesian space error from joint space with the tracking error theoretically eliminated. Third, in consideration of the uncertainty at the dynamics level, a multi-index optimization acceleration-level joint-drift-free scheme is presented to reveal the influence of dynamics factors on the redundant manipulator control. Afterwards, theoretical analyses are provided to prove the stability and feasibility of the corresponding dynamic neural network with the tracking error deduced. Then, computer simulations, performance comparisons, and physical experiments on different redundant manipulators synthesized by the proposed schemes are conducted to demonstrate the high performance and superiority of the NALJDF scheme and the influence of dynamics parameters on robot control. This work is of great significance to enhance the product quality and production efficiency in industrial production.