Omnidirectional Autonomous Aggressive Perching of Unmanned Aerial Vehicle using Reinforcement Learning Trajectory Generation and Control

Micro aerial vehicles are widely being researched and employed due to their relative low operation costs and high flexibility in various applications. We study the under-actuated quadrotor perching problem, designing a trajectory planner and controller which generates feasible trajectories and drives quadrotors to desired state in state space. This paper proposes a trajectory generating and tracking method for quadrotor perching that takes the advantages of reinforcement learning controller and traditional controller. We demonstrate the performance of the trained reinforcement learning controller generated trajectory information and manipulated quadrotor toward the perching point (manually throwing it up in the air with an initial velocity of 1 m/s). We show that this approach permits the control structure of trajectories and controllers enabling such aggressive maneuvers perching on vertical surfaces with relatively accurate. Computation time of evaluating the policy is only 0.03 sec per trajectory, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.