Autonomous Multi-UAV Exploration and Rescue in Extreme Environments

DOI

10.2991/978-94-6239-616-6_45How to use a DOI?

Keywords

Autonomous UAV navigation; Extreme environments; Multi-agent deep reinforcement learning; Swarm robotics

Abstract

Autonomous navigation in extreme hazardous environments where human access is impossible remains a critical challenge for unmanned aerial vehicles (UAVs). This paper presents a novel Hierarchical Multi-Agent Deep Reinforcement Learning (HMA-DRL) framework for coordinated UAV swarms capable of exploring and navigating through disaster zones, confined underground spaces, and chemically contaminated areas. Building upon recent advances in Deep Proximal Policy Optimization (D-PPO) and multi-agent coordination, our approach integrates Enhanced Perception Networks (EPN), Adaptive Communication Protocols (ACP), and Risk-Aware Decision-Making (RADM) modules. The system combines monocular depth estimation, thermal imaging, and gas detection sensors for comprehensive environmental awareness. Experimental validation in simulated disaster scenarios demonstrates 89% improvement in exploration coverage, 76% reduction in collision rates, and successful navigation through spaces with visibility less than 2 meters. Real-world testing in abandoned underground facilities shows the system’s capability to autonomously map and navigate 450+ meter tunnel networks while maintaining swarm cohesion and avoiding hazardous gas concentrations above 50 ppm.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - R. Jayalakshmi
AU  - Mohamed B. Sirajuddeen
AU  - S. Pragadeeswar
PY  - 2026
DA  - 2026/03/31
TI  - Autonomous Multi-UAV Exploration and Rescue in Extreme Environments
BT  - Proceedings of the International Conference on Artificial Intelligence and Secure Data Analytics (ICAISDA 2025)
PB  - Atlantis Press
SP  - 600
EP  - 611
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-616-6_45
DO  - 10.2991/978-94-6239-616-6_45
ID  - Jayalakshmi2026
ER  -