Swarm algorithms for UAV route planning: a systematic review of characteristics, classification, and operational performance
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Abstract
Path planning for unmanned aerial vehicles (UAVs) using swarm algorithms is a central topic in autonomous robotics. However, the literature still lacks systematic reviews that jointly address algorithm classification, operational characteristics, and performance evaluation. This study proposes a five-category taxonomy, a domain-based frequency analysis, and a nine-metric evaluation framework. Following PRISMA guidelines, searches were conducted in IEEE Xplore, Scopus, ScienceDirect, and ACM Digital Library between November and December 2025. From an initial set of 2,761 records, 31 articles were included, comprising 25 primary studies and 6 systematic reviews. PSO, ACO, and ABC account for 66% of the 56 identified algorithm appearances, with prominent applications in defense, search and rescue, and agriculture. Hybrid methods emerged as the main research trend, while AI-based approaches show the greatest potential for scalability and autonomous adaptation. The reviewed literature prioritizes path length (96%) and convergence time (88%), whereas energy efficiency (56\%) and area coverage (48%) remain comparatively underexplored.
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