Research On UAV Path Planning Technology Based On Swarm Intelligence Algorithms

The path planning of UAV is defined as calculating the optimal or sub-optimal executable flight trajectory from the initial position to the termination position of UAV when the dynamic constraints and some performance indicators are satisfied.Path planning,as a key technology of UAV mission planning system,has always been one of the key research issues of UAV experts in various countries.In the actual environment,the flight environment of UAV is very complex,and there are many constraints to be met.The effect of path planning depends not only on the merits of the algorithm,but also on the strategy of solving the problem.Therefore,how to accurately model the environment and select appropriate and efficient planning algorithm are the key elements to solve UAV path planning technology.Based on particle swarm optimization(PSO),simulated annealing(SA)and artificial fish swarm optimization(AFSA),the path planning of UAV in three-dimensional environment is studied in this thesis.The results achieved include the following aspects:(1)The mathematical model of UAV path planning is established.Firstly,UAV path planning modeling method,UAV maneuverability constraints and UAV flight threat category constraints are studied and analyzed,and the equivalent three-dimensional digital map is established.Among them,mathematical modeling focuses on the construction of three-dimensional digital map of UAV flight.A three-dimensional mathematical model for simulating the real environment is formed by synthesizing the reference terrain,peaks and some threatening environmental information in the flight environment.It paves the way for the following swarm intelligence algorithm to carry out static and dynamic path planning.(2)Research on Global Static Path Planning.In static situation,PSO-SA algorithm is proposed to overcome the shortcoming that particle swarm optimization is easy to fall into local extremum.The particle state updating method is improved by using simulated annealing jump probability strategy.The simulation results of MATLAB show that the algorithm can increase the global search ability of the path,reduce the path planning time,and achieve static path optimization.(3)Research on Dynamic Online Path Planning.In dynamic situation,PSO-AFSA algorithm is proposed to solve the problem of unmanned aerial vehicle(UAV)route adjustment in time when it encounters unexpected situations in flight.Combining the foraging,tail-chasing,clustering and random walk behavior of artificial fish swarm algorithm,the artificial fish swarm optimization strategy is introduced into the particle swarm optimization algorithm.The behavior of particles is selected by the behavior evaluation strategy of artificial fish swarm,and the global extremum of particles is updated continuously through bulletin boards.The simulation results of MATLAB show that the algorithm can effectively overcome the premature phenomenon of particle swarm optimization and has a fast convergence speed.And in the three-dimensional dynamic environment,the algorithm can quickly plan a compact and safe UAV path.