Abstract
The problem of inverse kinematics in serially manipulated upper limb rehabilitation robots involves deducing joint rotation angles from the position of the end-effector. Unlike forward kinematics, inverse kinematics lacks systematic solution approaches, and it is especially challenging for certain robot morphologies. This study proposes a deep learning-based model to estimate joint angles from a specified end-effector position. The model shows considerable effectiveness in calculating joint angles for a variety of target positions. The enhanced position-tracking capability of the proposed algorithm than existing analytical methods will enable the development of efficient controllers in future.