A Reinforcement Learning-Based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

Fereidoun H. Panahi, Farzad H. Panahi, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This article discusses the use of unmanned aerial vehicles (UAVs) to detect, warn, and suppress forest fires. The proposed system consists of: 1) an energy harvesting-assisted surveillance UAV (SUAV) that detects and reports fires and 2) multiple ground-based firefighting UAVs (FUAVs) that travel to fire spots to suppress the fire. First, the SUAV carries out the forest fire detection using the onboard color and infrared cameras. The SUAV then uses fire alarm signals to warn/inform the ground-based FUAVs to begin the firefighting mission. The goal is to inform as many FUAVs as possible as the required task (extinguishing the fire) can be completed more effectively and faster with more FUAVs. To do this, the SUAV with onboard processing capabilities makes use of the advantages of reinforcement learning. In particular, we propose a double Q-learning-based trajectory design problem that enables the energy-constrained SUAV to find the optimal sequence of FUAVs to visit (optimal flying trajectory) in order to maximize the number of informed FUAVs while considering the limited execution time, as the crucial issue is to control fire from the beginning to prevent uncontrolled spread of fire.

Original languageEnglish
Article number5500216
JournalIEEE Transactions on Instrumentation and Measurement
Publication statusPublished - 2023


  • Energy harvesting (EH)
  • fire warning and suppression
  • mission success metric
  • reinforcement learning (RL)
  • trajectory optimization
  • unmanned aerial vehicles (UAVs)

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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