Trajectory Planning of Deep-sea Electric Manipulator Based on Energy Optimization

The dynamic model of the deep-sea electric manipulator is complex, so it is difficult to construct an accurate objective function of energy optimization based on dynamic model. Therefore, a method to establish power model of the manipulator is proposed, using radial basis function (RBF) neural network. Firstly, the RBF neural network is trained by using the experimental data set of underwater motion of the manipulator. By utilizing the power model based on the RBF neural network, the energy objective function of the manipulator is established combined with the trajectory planning polynomial of the manipulator joint space. Then, the adaptive particle swarm optimization (PSO) algorithm is used to solve the optimal trajectory parameters. The results show that the root mean square error (RMSE) of RBF power network is 20.89 W, and the energy consumption based on the optimized trajectory is 410.8 J (18.3%) lower than the average energy consumption based on the the experimental trajectory. The experimental results show that the trajectory planning method based on the adaptive PSO algorithm achieves the goal of energy optimization.