Collision-Aware UAV Trajectories for Data Collection via Reinforcement Learning

Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu

Research output: Contribution to journalConference Articlepeer-review

2 Scopus citations


Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories in multi-UAV non-cooperative scenarios is a challenging task. In this paper, we consider a path planning optimization problem to maximize the collected data from multiple Internet of Things (IoT) nodes under realistic constraints. The considered multi-UAV non-cooperative scenarios involve random number of other UAVs in addition to the typical UAV, and UAVs do not communicate with each other. We translate the problem into an Markov decision process (MDP). Dueling double deep Q-network (D3QN) is proposed to learn the decision making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

Original languageEnglish (US)
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2021
Externally publishedYes
Event2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
Duration: Dec 7 2021Dec 11 2021


  • collision avoidance
  • Data collection
  • deep reinforcement learning
  • multi-UAV scenarios
  • path planning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


Dive into the research topics of 'Collision-Aware UAV Trajectories for Data Collection via Reinforcement Learning'. Together they form a unique fingerprint.

Cite this