The invention discloses an unmanned aerial vehicle obstacle avoidance and path planning method, which combines Monte Carlo tree search and contrast reinforcement learning algorithms, overcomes the problem of insufficient signals of a GPS (Global Positioning System) in a specific environment, and realizes the obstacle avoidance and path selection functions of an unmanned aerial vehicle in a complex environment. The method includes the following steps: (1) constructing an environment simulator; (2) the unmanned aerial vehicle obtains observation information in the simulator, and the observation information is processed by using a deep neural network; (3) performing coarse-grained path planning by utilizing Monte Carlo tree search, and generating stage target points in the advancing path of the unmanned aerial vehicle for training of a subsequent reinforcement learning algorithm; (4) learning a fine control strategy and fine-grained path planning of the unmanned aerial vehicle by using reinforcement learning; (5) accelerating unmanned aerial vehicle training based on comparative learning. According to the method provided by the invention, the unmanned aerial vehicle has an autonomous decision-making capability in a complex environment with high difficulty coefficient and large uncertain factors, and can cope with emergencies to a certain extent to complete specific tasks.
Unmanned aerial vehicle obstacle avoidance and path planning method