Drone-Based Search Algorithms Inspired by Ant Colonies

Efficient search, mapping, and coverage of large or uncertain environments remain fundamental challenges in unmanned aerial vehicle (UAV) operations, particularly in disaster response, surveillance, and environmental monitoring. Centralized planning and single-drone strategies suffer from scalability limits, communication bottlenecks, and vulnerability to individual agent failure. This paper presents a drone-based search and mapping framework inspired by ant colony intelligence, translating biological principles such as pheromone-based exploration, stigmergy, decentralized decision-making, and adaptive path reinforcement into deployable UAV swarm algorithms. Without relying on heavy mathematical formulations, the study develops a practical, engineering-oriented algorithmic architecture in which UAVs coordinate indirectly through virtual pheromones embedded in shared or local maps. The proposed approach demonstrates inherent robustness, scalability, and adaptability to dynamic environments, obstacles, and drone failures. The paper’s primary contribution lies in presenting a realistic, implementable ant-inspired swarm search system suitable for real-world UAV missions, bridging the gap between bio-inspired theory and operational aerospace robotics.