%0 Journal Article %A GAO Jingpeng %A HU Xinyu %A JIANG Zhiye %T Unmanned Aerial Vehicle Track Planning Algorithm Based on Improved DDPG %D 2022 %R 10.3778/j.issn.1002-8331.2106-0054 %J Computer Engineering and Applications %P 264-272 %V 58 %N 8 %X An improved DDPG flight track planning algorithm is proposed, aiming at the problem of high processing complexity of intelligent algorithm due to unknown threats in UAV flight process which leads to the difficulty of real-time flight track planning, and long training time by adjusting the parameters of DDPG algorithm in deep reinforcement learning. The flight scene model is established under the background of UAV track planning. According to the flight dynamics theory, the action space is built. On the basis of the non-sparse idea, the reward function is designed. Combined with the artificial bee colony algorithm, the updating mechanism of the model parameters of DDPG algorithm is improved, and the network model is trained to achieve the flight track decision-making of UAV. Simulation results show that the overall training time of the proposed algorithm is only 1.98 times of the average training time of the prototype algorithm, the training efficiency is improved, and the cost of time is reduced. Besides, under the condition of satisfy real time flight, the proposed algorithm can meet the demand of UAV track quality, and provides a new idea for promoting the practical application of deep reinforcement learning in flight track planning. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2106-0054