Abstract: In order to improve the efficiency of driverless trucks in open-pit mines and realize 24-hour continuous and safe mining, an improved ant colony algorithm (ACO) method for solving the unmanned truck scheduling problem was proposed. By improving the state transition probability formula and pheromone updating strategy, and introducing the crossover mutation operator of genetic algorithm to improve the performance of ant colony algorithm. Finally, the improved ant colony algorithm was used to solve the scheduling process of unmanned truck in an open-pit mine. The results show that the convergence speed of the improved ant colony algorithm is faster than that of the ant colony algorithm and genetic algorithm, and the running time is the shortest; The optimization results obtained by considering different factors as the objective function in the transportation process of trucks are different, taking the minimum comprehensive cost as the optimization objective, the energy consumption is the least.