The invention discloses a real-
time path planning method for an unmanned aerial vehicle based on deep
reinforcement learning. The method comprises the steps of S1, obtaining the current environment state of the unmanned aerial vehicle from a
simulation environment, calculating the
threat degree of a target object defense unit to the unmanned aerial vehicle according to a situation evaluation model, and constructing a situation map of a task area of the unmanned aerial vehicle; constructing a main network and a target network of the
convolutional neural network and the competitive neural network to perform
action selection; S2, obtaining the current environment state of the unmanned aerial vehicle according to the
communication link, calculating a
threat value of the target object defense unit to the unmanned aerial vehicle according to the situation evaluation model, constructing a situation map of the task areas of the unmanned aerial vehicle, constructing a competitive dual-Q network, loading the trained
network model, evaluating the Q value of each action in the current state, selecting the action corresponding to the maximum Q value, determining the
flight direction of the unmanned aerial vehicle, and completing the flight task. According to the invention, the autonomous decision-making ability of the unmanned aerial vehicle can be effectively improved, and the method has high robustness and application value.