CNN-Based Inverse Kinematics Analysis and Trajectory Planning for Rehabilitation Robotics
To enhance the comfort of upper limb rehabilitation training for stroke patients,a motion-com-pliant upper limb rehabilitation robot is designed.The robot model was established using the Denavit-Harten-berg(D-H)parameter method,and positive kinematic analysis was performed.Given the high complexity of traditional inverse kinematics solving methods,a convolutional neural network was implemented to solve the in-verse kinematics.The neural network achieved the accuracy of 96.24%in predicting joint angles,with errors in joint positions within the range of 0 to 0.025 meters compared to the labeled positions.Analyzing the robot's workspace using analytical methods,a set of rehabilitation training movements was planned using a seventh-de-gree polynomial interpolation method.Simulation results meet the rehabilitation exercise requirements of pa-tients,enabling the provision of high-quality rehabilitation training.
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