UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning

@article{Theile2021UAVPP,
  title={UAV Path Planning using Global and Local Map Information with Deep Reinforcement Learning},
  author={Mirco Theile and Harald Bayerlein and Richard Nai and David Gesbert and Marco Caccamo},
  journal={2021 20th International Conference on Advanced Robotics (ICAR)},
  year={2021},
  pages={539-546}
}
Path planning methods for autonomous unmanned aerial vehicles (UAVs) are typically designed for one specific type of mission. This work presents a method for autonomous UAV path planning based on deep reinforcement learning (DRL) that can be applied to a wide range of mission scenarios. Specifically, we compare coverage path planning (CPP), where the UAV's goal is to survey an area of interest to data harvesting (DH), where the UAV collects data from distributed Internet of Things (IoT) sensor… 

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