Abstract: Aiming at the slow convergence and large error issues in solving the inverse kinematics of a snake-arm robot using traditional BP neural networks,a progressive BP neural network inverse kinematics solution was proposed. Firstly,a forward kinematics model was established. Then,the principle of optimal compliance was introduced. A weighted sum of joint angle changes was used as the penalty term in the loss function,ensuring continuity and smoothness of the joint angle changes in the robot′s joint movements. Finally,based on the constraints between the joints of the snake-arm robot,a BP neural network model with a hierarchical progressive structure was constructed to improve the solving speed.Simulation results show that the progressive BP neural network achieves high accuracy and fast convergence,effectively solving the inverse kinematics problem of the snake-arm robot.

Key words: snake-arm robot, inverse kinematics, BP neural network