Research on Global Path Planning of Unmanned Sailboat Based on Improved Ant Colony Optimization

When global environment of unmanned sailboat is known, in order to obtain a feasible and reasonable path planning, an improved ant colony optimization (IACO) based on the movement characteristics of unmanned sailboat was proposed. In environmental modeling, special steering rules was set for unmanned sailboats to ensure better obstacle avoidance performance in global environment. Propose an improved ant colony heuristic information function, introducing path length factors, wind field height factors, and turning frequency that can be artificially limited. Propose an improved pheromone update rule, expecting the ant colony to choose a global path with a shorter path length and a smaller wind field height standard deviation. Set the pheromone threshold to prevent ant colony system from falling into a stagnant state, and limit the dynamic volatilization coefficient of pheromone to improve convergence of the algorithm. Simulation results verify that the optimal global path of unmanned sailboat can be obtained by improving the ant colony optimization which also has better convergence.