Kinematic Modeling of a 7-DOF Tendon-Like-Driven Robot Based on Optimization and Deep Learning

Abstract

This paper proposes a novel 7-DOF tendon-like-driven redundant robot (TDR7) based on a weighted inverse kinematics (IK) optimization algorithm and a deep learning fine-tuning model. The robot features a modular design that enables highly flexible movements of the shoulder, elbow, and wrist joints. Its kinematic model is established using the Denavit-Hartenberg (D-H) parameter method. To address the complexity of solving IK for 7-DOF redundant robots, a weighted gradient projection method specialized for TDR7 (SWGPM-TDR7) is introduced. This algorithm integrates joint constraints, singularity avoidance, and minimum energy consumption into a multi-objective optimization framework, significantly improving joint motion continuity and trajectory planning efficiency while maintaining solution accuracy. To further accommodate complex trajectory planning requirements, a deep learning fine-tuning model (RWKV-TDR7) that combines recurrent networks with self-attention mechanisms is introduced. Through fine-tuning, RWKV-TDR7 achieves efficient trajectory fitting for TDR7, supports long-sequence outputs, and reduces computational complexity. Simulation and experimental validations demonstrate that the robot exhibits excellent performance in forward kinematics, inverse kinematics, and trajectory tracking in terms of accuracy, stability, and continuity. This work provides an effective solution for the design of high-performance robotic systems in medical and industrial applications.