Automatic landing method for quad-rotor helicopter based on Markov decision process

When using only civilian GPS guidance for automatic landing of quad-rotor helicopters, there exists the problem of significant deviation. A guidance method based on Markov decision is designed to effectively improve the accuracy of automatic landing. Firstly, an alignment method based on the improved artificial potential field algorithm is designed. The AprilTag3 recognition algorithm is used to quickly identify the landing tag and build the potential field at its center. The yaw alignment of aircraft and pitch alignment of gimbal are performed under the gravitational pull. Secondly, the landing process is established as a Markov Decision Process (MDP) model. The state transition relationship among the action set, state set, and reward set is designed to prevent the aircraft from deviating from the expected state. Finally, considering the environment of automatic landing, an information set is added to the MDP model. The information during flight is used to assist in the determination of the transition relationship, so as to improve the decision-making accuracy of states and actions. Actual flight verification is carried out on the ANAFI quad-rotor helicopter platform. The experimental results show that the automatic landing guidance method based on the MDP model can effectively suppress excessive movements and misjudgments of the aircraft during the landing process. The landing error has been reduced from the meter level to about 10 centimeters, meeting the accuracy requirements for automatic landing of quad-rotor helicopters.