UAV Path Planning Research Based On Modify Quantum Particle Swarm Optimization Algorithm

With the continuous progress of the national economy,the UAV path planning,as an important part of the task implementation whether in military or civil aspects,has attracted people's attention,and many scholars focus on the study.Our country is a member of the IMO category A,also a navigational power,in order to adapt to the changes in the complexity and diversity of today's ocean affairs,just rely on the traditional ship's original equipment is not enough to meet and adapt to today's complex marine environment and information needs.Therefore,the emergence of UAV for the ship's maritime security(such as navigation on a regular basis),the risk of anti-fouling(such as oil spills,sailing accident evidence),emergency search and rescue(such as search and positioning,accident investigation)guarantee.In this paper,we mainly study and analyze the improved QPSO algorithm and its application in UAV path planning.The main contents are as follows:1)Firstly,the objective function model of UAV path is established.Secondly,it analyzes whether the model is a multimodal function or a unimodal function,and provides the main basis for the selection of important parameters.Details as follows according to the definition of convex function,take the objective function of fixed multi-dimensional and seeked twodimensional method to analyze whether it is a convex function.Mainly through theoretical analysis and Matlab simulation to verify two methods to analyze.And according to the conclusion,the properties of the objective function are obtained.2)Based on QPSO algorithm,the improved QPSO algorithm is proposed to dynamically update the distance of the local attractor and the feasible solution boundary.And it is verified by the standard test function.The improved algorithm is based on the detection distance of the particle to shrink or expand the shrinkage expansion factor,which is different from the shrinkage expansion factor in the QPSO algorithm.3)The convergence of QPSO algorithm is analyzed and proved by the mathematical method of probability convergence.Based on the convergence of the standard QPSO algorithm,this paper analyzes and validates the improved QPSO algorithm by probability convergence method.4)In this paper,MQPSO and particle swarm optimization algorithm,QPSO,quantum particle swarm optimization algorithm with Gaussian and other algorithms are applied to UAV path planning,and algorithms which are a variety of terrain simulation verification.And the comparison and analysis of the verification results to prove the feasibility and effectiveness of the proposed algorithm.