Research On Cooperative Drone Delivery Algorithm Under Multiple Constraints

In recent years,the last one-kilometer Logistics problem in delivery directly affects the quality of service.The last one-kilometer Logistics is the final process of delivery.How to effectively use UAV as the last mile of logistics distribution has become a hot research issue in the industry.However,there still are some problems such as UAV path planning and coordinated scheduling need to be solved.This paper studies the UAV route planning algorithm and the unmanned aircraft cooperative scheduling algorithm.To solve the problem of UAV route planning,bat algorithm with fast convergence and high robustness is adopted and the following three improvements are proposed: First,the robustness of the adaptive inertia weight increases algorithm based on the optimization success rate is introduced.Second,logistic mapping prevents algorithms from falling into local optimum.Third,the improved artificial potential field method is used to accelerate the convergence speed of the algorithm.In this paper,the simulation experiments of UAV route planning algorithm are proposed in two-dimensional and three-dimensional terrain environment respectively.The simulation results show that the improved bat algorithm proposed in this paper is applied to the UAV route planning problem.Its route length,obstacle avoidance and convergence rate are superior to bat algorithm and differential bat algorithm.To solve the cooperative task scheduling problem of UAV,the ant colony algorithm with high convergence precision and parallelism is adopted and the following four improvements are proposed: First,the elite individual strategy accelerates the convergence process of the algorithm.Second,the adaptive pheromone heuristic factor and expectation inspiration are adopted,which prevent algorithm from falling in local optimum.Third,the robustness of the adaptive pheromone volatilization factor based on Gaussian mutation is proposed.Fourth,the probability migration strategy based on weighted cost factor is introduced to increase the applicability of the algorithm.This paper generates the logistics center and the receiving location from the real floating car data,generates the distribution task,and uses the improved ant colony algorithm to carry out the coordinated distribution of the drone.The simulation results show that the improved ant colony algorithm is better than the particle swarm algorithm and the genetic algorithm in terms of route length and variance under the same conditions.