Inverse kinematics solution for a six-degree-of-freedom upper limb rehabilitation robot using deep learning models
Shah M.F. Khan N.A. Jamwal P.K. Chetty G. Goecke R. Hussain S.
June 2025Springer Science and Business Media Deutschland GmbH
Neural Computing and Applications
2025#37Issue 1812991 - 13009 pp.
The inverse kinematics problem in serially manipulated upper limb rehabilitation robots implies the usage of the end-effector position to obtain the joint rotation angles. In contrast to the forward kinematics, there are no systematic approaches for solving the inverse kinematics problem. Furthermore, for some morphology of the upper limb rehabilitation robots, the inverse kinematics problem is particularly challenging to solve. Conventional methods to solve the inverse kinematics problem reported in the literature are computationally expensive. In the present work, we propose a deep learning-based model to acquire the joint angles for a given end-effector position. The proposed approach exhibits high efficacy in determining the joint angles for various target positions and can accurately predict the end-effector positions once trained, improving the ability of the upper limb rehabilitation robot to adapt to varying patient needs. Due to its improved capability and effectiveness to track positions, the proposed algorithm lays the foundation for the development of efficient controllers in future.
Deep learning , Forward kinematics , Inverse kinematics , Neural networks , Rehabilitation robots , Upper limb
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School of Information Technology & Systems, University of Canberra, Canberra, Australia
Department of Electrical and Computer Engineering, Nazarbayev University, Astana, Kazakhstan
School of Systems and Computing, University of New South Wales, Canberra, Australia
School of Information Technology & Systems
Department of Electrical and Computer Engineering
School of Systems and Computing
10 лет помогаем публиковать статьи Международный издатель
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