首页|Real-time UAV path planning based on LSTM network
Real-time UAV path planning based on LSTM network
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NETL
NSTL
万方数据
维普
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long short-term memory(RPP-LSTM)network is proposed,which com-bines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM net-works are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM net-work,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that com-pared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environ-ments and has significantly improved robustness and accuracy when performing UAV real-time path planning.
deep Q networkpath planningneural networkunmanned aerial vehicle(UAV)long short-term memory(LSTM)
NETL
NSTL
万方数据
维普
