Route Planning of Unmanned Ship Based on Multi - step Adaptive Ant Colony Algorithm

When the unmanned ship sails in the unknown sea area, it is necessary to plan the track for the sea environment. However, the traditional ant colony algorithm has slow convergence speed and a large number of turning points in its planning results, which is not in line with the actual motion state of the ship. It is necessary to optimize and improve. Firstly, the information entropy is introduced to quantitatively calculate the solution diversity, and an adaptive pheromone updating strategy based on solution diversity is designed according to the calculation results. Then, in order to further improve the convergence rate and reduce a large number of invalid turning points, a state transition rule based on multi-step search and smoothness is designed on the basis of the state transition rule of the ant colony system. Finally, the simulation results show that the average smoothness of the improved algorithm is 63% and 65% less than that of the comparison algorithm. the average voyages are 7% and 12% less respectively; the average convergence rates are 9.6% and 54.2% less, respectively, with good convergence rate and track planning quality.