A UAV Path Planning Method Based on Transfer Learning Strategy Deep Q-Network

Abstract

The present invention discloses a UAV path planning method based on a transfer learning strategy deep Q network. The method first uses the grid method to model and describe the dynamic environment in which the UAV is located, and establishes the state space of the UAV. and the action space model; secondly, initialize the network parameters of DQN and the current state of the UAV; then, under the static environment model, use the reward mechanism based on the social force model to train the DQN to obtain the network weight and optimal action value; Then, use transfer learning to transfer the network weights and optimal action values ​​trained in the static environment to the dynamic environment, continue the neural network training, and get the actions to be executed by the UAV; finally, calculate the position of the UAV at the current moment, Realize the path planning of UAV in dynamic environment. The invention effectively solves the problems of slow DQN training convergence speed, unsatisfactory path planning and low success rate when the UAV performs path planning in a dynamic environment.

Description

technical field [0001] The invention belongs to the field of unmanned aerial vehicle path planning, in particular to a method for unmanned aerial vehicle path planning based on transfer learning and DQN (Deep Q-Network), applying transfer learning and deep reinforcement learning for unmanned aerial vehicle path planning in a dynamic environment . [0002] technical background [0003] UAV path planning is the core issue in the field of UAV technology research, and related algorithms are developing rapidly. Traditional methods include: Dijkstra shortest path search method (greedy algorithm), A* algorithm, ant colony optimization algorithm, reinforcement learning algorithm, etc. The core idea of ​​Dijkstra's algorithm is that the next vertex selected for each exploration is the point with the closest Euclidean distance from the starting point until the target is found. This method is only suitable for known overall information and static maps, and the efficiency is low; the A...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a UAV path planning method combining transfer learning and DQN algorithm, which can solve the problems of slow convergence speed and low success rate when DQN algorithm performs path planning in a dynamic environment

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