A bio-inspired swarm UAV framework integrating thermal sensing and optimization-based coordination for efficient search and rescue operations

TL;DR

This paper introduces a bio-inspired swarm of drones using thermal sensing and optimization to improve search and rescue efficiency in disaster zones.

Contribution

A novel modular framework for UAV swarms with a new Exploration Score metric for evaluating thermal search efficiency.

Findings

PSO achieved the highest exploration score of 0.67, outperforming other bio-inspired algorithms.

PSO reached 80% area coverage in 60% of mission time with low redundancy and balanced drone separation.

The framework enables fast, adaptive thermal search missions, improving early survivor detection probability.

Abstract

Search and rescue (SAR) operations demand rapid, reliable detection of survivors in disaster-stricken environments where time, terrain, and responder safety are critical constraints. While Unmanned Aerial Vehicles (UAVs) equipped with thermal imaging offer a promising aerial solution, current approaches often struggle to balance large-area coverage, responsiveness to thermal cues, and avoidance of redundant search paths. This paper proposes a modular, bio-inspired swarm UAV framework that enables real-time thermal-based SAR through autonomous, cooperative exploration of a discretized search grid. Each UAV operates as an intelligent agent, leveraging bio-inspired optimization algorithms to determine its next target location, with decision-making grounded in a shared thermal confidence map. A novel Exploration Score metric is introduced to quantitatively assess search efficiency by integrating area coverage, redundancy minimization, and spatial dispersion of the swarm. The system is implemented in a high-fidelity PX4 + Gazebo simulation environment, featuring real-time thermal detection and multi-drone coordination. Ten bio-inspired algorithms are evaluated under identical conditions, with Particle Swarm Optimization (PSO) achieving the highest exploration score of 0.67, outperforming Grey Wolf Optimizer (0.62) and Ant Colony Optimization (0.59). PSO also achieved 80% area coverage within 60% of the mission time while maintaining a low redundancy ratio (~ 0.25) and balanced inter-drone separation (15–20 m). These results confirm the framework’s effectiveness in enabling fast, adaptive, and energy-efficient thermal search missions, significantly enhancing the probability of early survivor detection. The proposed architecture serves as a benchmarking platform for UAV swarm coordination, and the exploration score metric offers a unified performance measure for future SAR algorithm comparisons.

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