An Intelligent Planning Method For The Multi-Rotor Manipulation Robot With Reinforcement Learning

In this paper, an intelligent planning method based on proximal policy optimization algorithm for the multi-rotor aerial manipulation robot is presented. This method can not only avoid the complexity of the dynamic analysis and modeling but also the large disturbance of the manipulator to the robot's body produced by the independent kinematic planning method. The disadvantage of kinematic planning method in aerial manipulation is given. The detailed structure of the adopted training and simulation environment is introduced. A deep reinforcement learning formulation is proposed to deal with the aerial manipulation of the robot. The particulars of setup in training and simulation are illustrated and the practical training and a series tests are carried out. The results of simulation proved the feasibility of this intelligent planning method and its advantages in real-time planning or replanning to enhance the stability of the robot in manipulation.