Research On Search And Rescue Optimization Of UAV Disaster Area Based On Swarm Intelligence Algorithm

As a manifestation of unmanned technology,UAVs have natural advantages in aerial reconnaissance,material transportation,and target area search which especially reflected in the disaster relief work.It can be used as a scouting device to monitor the rescue situation of the disaster area,and can also be used as a carrier for disaster relief materials,and transport the materials to the corresponding disaster area in accordance with the plan.Ant colony algorithm is a swarm intelligence algorithm.It mainly imitates the communication method of ant colony searching for food in the foraging process,and abstracts a probability algorithm for solving the optimization path in the figure.This paper first introduces the technology used in the UAV search and rescue scene in the disaster area,and analyzes the advantages of UAV and GIS in disaster relief work.Secondly,this paper starts from the optimization of ant colony algorithm,analyzes several aspects that ant colony algorithm can optimize,and proposes several optimization schemes from different angles.Then,this paper analyzes the feasibility of the ant colony algorithm in the search and rescue scene in the disaster area,and designs a closed loop search and rescue path that returns to the starting point after traversing all areas from a certain point,similar to traveling salesman problem.The ant colony algorithm has a comparative advantage in solving this problem.Then analyze the particularity of the UAV search and rescue scene in the disaster area from the details,and optimize the ant colony algorithm to make it perfect for this scenario.The parameters of the dynamic variability of the disaster area are optimized for the parameters,and two algorithms,the refined interval method and the particle cluster-based parameter optimization method,are proposed,which are applicable to different input scales respectively;Prioritize the affected area from the disaster degree of the disaster area,optimize the selection scheme of the algorithm for the next area,and discuss the value of the adjustment parameters for different input sizes;The UAV is classified from a functional perspective,and the particle swarm algorithm is used to optimize the position of the UAV acting as a temporary base station.Finally,this paper integrates all the improved schemes and proposes an Ant Colony Optimization with priority Parameter Adaptive(PAACO).After several sets of experimental simulation comparison and analysis,the PAACO in this paper does show good results.It can solve the path optimization problem of search and rescue in the disaster area of the UAVs.