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Towards Real-Time Path Planning through Deep Reinforcement Learning for a UAV in Dynamic Environments

  • Chao Yan
  • Xiaojia XiangEmail author
  • Chang Wang
Article

Abstract

Path planning remains a challenge for Unmanned Aerial Vehicles (UAVs) in dynamic environments with potential threats. In this paper, we have proposed a Deep Reinforcement Learning (DRL) approach for UAV path planning based on the global situation information. We have chosen the STAGE Scenario software to provide the simulation environment where a situation assessment model is developed with consideration of the UAV survival probability under enemy radar detection and missile attack. We have employed the dueling double deep Q-networks (D3QN) algorithm that takes a set of situation maps as input to approximate the Q-values corresponding to all candidate actions. In addition, the ε-greedy strategy is combined with heuristic search rules to select an action. We have demonstrated the performance of the proposed method under both static and dynamic task settings.

Keywords

Unmanned aerial vehicle (UAV) Path planning Reinforcement learning Deep Q-network STAGE scenario 

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Intelligence Science and TechnologyNational University of Defense TechnologyChangshaChina

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