Swarm Intelligence-Based Path Planning for Unmanned Surface Vehicles in Maritime Patrol Missions - Afrianta, Muhammad Fajar Indra; Dindin, Imanuel; Bagdja, Ade; Amperiawan, Gita; Furqon, Muhammad Zainal (Indonesian Journal of Advanced Research, 2026)
Autonomous maritime operations increasingly demand sophisticated navigation solutions for unmanned surface platforms conducting surveillance missions. This systematic review examines bio-inspired optimization techniques applied to route planning within multi-vessel autonomous systems during 2020-2025. Analysis encompasses twelve computational methods centered on Particle Swarm and Ant Colony Optimization, evaluating execution speed, solution accuracy, and operational applicability. Findings indicate PSO variants offer optimal equilibrium between processing efficiency and outcome precision, whereas combined methodologies demonstrate enhanced resilience within unpredictable marine conditions. Four coordination frameworks leader-follower, virtual structure, behavior-driven, and consensus mechanisms were assessed for tactical scenarios. Integration of international collision prevention regulations through adaptive safety zones shows substantial advancement, though 73% investigations utilize simulation exclusively without physical maritime testing. Results establish theoretical groundwork and performance standards for operationally deployable autonomous fleet patrol capabilities while highlighting research priorities including standardized assessment protocols, robust communication architectures, and comprehensive validation bridging simulation-reality gaps.