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Deep-Sarsa Based Multi-UAV Path Planning and Obstacle Avoidance in a Dynamic Environment

  • Wei Luo
  • Qirong TangEmail author
  • Changhong Fu
  • Peter Eberhard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

This study presents a Deep-Sarsa based path planning and obstacle avoidance method for unmanned aerial vehicles (UAVs). Deep-Sarsa is an on-policy reinforcement learning approach, which gains information and rewards from the environment and helps UAV to avoid moving obstacles as well as finds a path to a target based on a deep neural network. It has a significant advantage over dynamic environment compared to other algorithms. In this paper, a Deep-Sarsa model is trained in a grid environment and then deployed in an environment in ROS-Gazebo for UAVs. The experimental results show that the trained Deep-Sarsa model can guide the UAVs to the target without any collisions. This is the first time that Deep-Sarsa has been developed to achieve autonomous path planning and obstacle avoidance of UAVs in a dynamic environment.

Keywords

UAV Deep-Sarsa Multi-agent Dynamic environment 

Notes

Acknowledgements

This work is supported by the project of National Natural Science Foundation of China (No. 61603277), the 13th-Five-Year-Plan on Common Technology, key project (No. 41412050101), and the Shanghai Aerospace Science and Technology Innovation Fund (SAST 2016017). Meanwhile, this work is also partially supported by the Youth 1000 program project (No. 1000231901), as well as by the Key Basic Research Project of Shanghai Science and Technology Innovation Plan (No. 15JC1403300). All these supports are highly appreciated.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Wei Luo
    • 1
  • Qirong Tang
    • 2
    Email author
  • Changhong Fu
    • 2
  • Peter Eberhard
    • 1
  1. 1.Institute of Engineering and Computational MechanicsUniversity of StuttgartStuttgartGermany
  2. 2.Laboratory of Robotics and Multibody System, School of Mechanical EngineeringTongji UniversityShanghaiPeople’s Republic of China

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