Path Planning of Urban Logistics Delivery Unmanned Vehicles Based on Optimized Ant Colony Algorithm

This article proposes a path planning strategy for unmanned vehicles based on ant colony algorithm to address the problem of designing the shortest route for autonomous vehicles in cities. Firstly, this article provides a detailed introduction to the mechanism of ant colony algorithm, analyzes its advantages and disadvantages, and introduces optimization strategies. Then, based on the basic principles of the algorithm, a simulated city map is constructed to clear obstacles. Finally, on the basis of the simulated map, MATLAB simulation experiments are conducted by continuously adjusting the parameters of ant colony algorithm. After adjusting the parameters, the following conclusion can be drawn: when the number of ants is 75, the number of iterations is 120, the pheromone volatilization factor is 0.6, and the pheromone importance factor is 1.0. It is more in line with the expected requirements. Although this is only a simulation experiment, it provides important reference value for practical unmanned vehicle path planning.