Deep Reinforcement Learning for Time-Critical Wilderness Search and Rescue Using Drones

Abstract

Traditional search and rescue methods in wilderness areas can be time-consuming and have limited coverage. Drones offer a faster and more flexible solution, but optimizing their search paths is crucial for effective operations. This paper proposes a novel algorithm using deep reinforcement learning to create efficient search paths for drones in wilderness environments. Our approach leverages a priori data about the search area and the missing person in the form of a probability distribution map. This allows the policy to learn optimal flight paths that maximize the probability of finding the missing person quickly. Experimental results show that our method achieves a significant improvement in search times compared to traditional coverage planning and search planning algorithms by over 160 % , a difference that can mean life or death in real-world search operations Additionally, unlike previous work, our approach incorporates a continuous action space enabled by cubature, allowing for more nuanced flight patterns.

BibTex

 @article{ewers_deep_2025,
  title = {Deep Reinforcement Learning for Time-Critical Wilderness Search and Rescue Using Drones},
  author = {Ewers, Jan-Hendrik and Anderson, David and Thomson, Douglas},
  year = {2025},
  month = feb,
  journal = {Frontiers in Robotics and AI},
  volume = {11},
  pages = {1527095},
  issn = {2296-9144},
  doi = {10.3389/frobt.2024.1527095},
  urldate = {2025-02-04},
  copyright = {All rights reserved}
}