Multi-aircraft flight path planning method based on deep Q learning algorithm
A trajectory planning and learning algorithm technology, applied in the direction of instruments, three-dimensional position/course control, vehicle position/route/altitude control, etc., can solve the complexity, combination complexity and time complexity of multi-aircraft cooperative trajectory modeling Increase and other issues to achieve the ability to solve high-dimensional perception decision-making, high real-time performance, and enhance the overall combat effect
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[0049] Such as figure 1 As shown, according to an embodiment of the present invention, a multi-aircraft trajectory planning method based on a deep Q learning algorithm of the present invention includes:
[0050] S1. Construct a kinematic model of the aircraft based on the performance of the aircraft;
[0051] S2. Construct a mission model of the aircraft according to the target to be hit and based on a deep Q learning algorithm;
[0052] S3. Construct the state space of the aircraft according to the kinematic model and mission model;
[0053] S4. Construct the action space of the aircraft according to the kinematic model and mission model;
[0054] S5. Construct a neural network and reward function based on the deep Q learning algorithm based on the state space and the action space;
[0055] S6. Training the neural network based on the reward function;
[0056] S7. Perform target strike verification on the trained neural network.
[0057] According to an embodiment of the present inventi...
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