Combined classification and regression inverse kinematics modeling for overall shape prediction of soft manipulators

Abstract

In helping astronauts perform tasks, soft manipulators offer the advantages of softness, lightness, low cost, and interaction safety to interact with. However, modeling their inverse kinematics through traditional methods is subject to truncation errors, complex derivations and inexpressibility equations, owing to geometric singularities. Furthermore, facing the future control requirement for multiple major feature points on manipulators, most neural network approaches that solve the drive space only by the inverse solution of the end task space, cannot obtain the inverse kinematics solutions of all feature points, due to their including redundancy (the dimension n of the drive space of feature points is larger than that of the end task space). Therefore, this paper proposes a method combining classification and regression based on multi-layer perceptron for inverse kinematic modeling, namely the IK-MLP-CR method, with the bending states classified and the whole manipulator shape predicted, greatly reducing the amount of training data and training time. Finally, the performance evaluation of the proposed model is validated through the simulation and ground microgravity experiments. Results show that using the IK-MLP-CR method results in a successful grasping rate of over 97%, and that the trend of the experimental bending conforms to physical motions under different pressures.