A learning strategy from demonstration for the operation tasks of space manipulators

Abstract:

To improve the ability of overcoming the spatial disturbance, and reduce the joint torque fluctuations and energy consumption during operation, a learning strategy from demonstration based on dynamics constraints for space manipulators is proposed. This strategy is divided into two phases. Phase 1 is Gaussian process-based learning from demonstration, in which the motion model of the task is obtained by utilizing Gaussian process based on the kinesthetic demonstrations. Then, the desired trajectory distribution of the current task is reproduced using the model according to the environment. Phase 2 is the design of dynamics-constraint-based controller. The input of this controller is the trajectory distribution from phase 1, and the outputs are the joint desired torques. This controller is used to generate smoother joint control torques, while ensuring that the trajectory of manipulator can meet the task requirements. Finally, the strategy is verified by the on-orbit locating bolts task with Tiangong-2 space manipulator. Compared with the strategy of traditional learning from demonstration combined with computed torque controller, the joint torques peak-peak value of the large load joint is reduced by 45%, the number of peaks is reduced by 40%, and the energy consumption is reduced by 31%. Besides, the joint torques, accelerations and velocities are much smoother.