Intelligent kinematic physics engine construction for hyper-redundant cable-driven flexible manipulator

To solve the problems of difficult kinematics modeling and poor real-time performance for a hyper-redundant cable-driven flexible manipulator, a construction method of an intelligent kinematics physics engine based on Bayesian-optimized Long Short-Term Memory (BO-LSTM) network is proposed in this paper. Firstly, the multilevel kinematics equation of the flexible manipulator is established based on the Denavit-Hartenberg (D-H) parameter method, and an analytical model of the forward solution is established. Secondly, the inverse kinematic model of the Newton iteration method is proposed based on the kinematics equation, and the stability of the numerical solution method is proven. Subsequently, the kinematics database is established based on the Monte Carlo method, the analytical model of forward solution and the high-precision numerical model of inverse solution, and the database is visualized. Finally, an intelligent kinematics physics engine is established based on the kinematics database and the BO-LSTM neural network model. This model combines the global search ability of Bayesian optimization and the advantages of LSTM neural network in processing time series data, which provides a new solution for the kinematic modeling of hyper-redundant cable-driven flexible manipulator. The calculation speed of the forward and inverse BO-LSTM models are 0.018 s and 0.017 s, which are faster than the numerical method. The MSEs of the forward and inverse BO-LSTM models are 0.065 and 0.056 respectively, which are lower than those of the BP, RBF and LSTM neural networks. The SimMechanics module in MATLAB is used to simulate the hyper-redundant cable-driven flexible manipulator. The experimental result shows that the intelligent kinematics physics engine based on the BO-LSTM neural network model has high precision and high efficiency in kinematics solution.