Intelligent Environment-aware Path Planning Based on Ant Colony Optimization for Unmanned Sailboats in Complex Marine Environments

This study introduces an improved Ant Colony Optimization (IACO) strategy for autonomous sailboat routing, designed to incorporate wind and ocean current information for enhanced path planning in dynamic marine environments. Enhancing the traditional Ant Colony Optimization algorithm framework, IACO incorporates composite heuristics—wind alignment rewards, current synergy incentives, and obstacle avoidance penalties—to improve navigational feasibility and safety. A pheromone update mechanism based on elite path reinforcement and environmental adaptability further strengthens global search and accelerates convergence. Simulation results in both randomized and real-world maritime grid environments demonstrate that IACO produces shorter, smoother paths with higher planning efficiency, showcasing its strong practical value and environmental adaptability.