An Optimization Algorithm For Improved Hybrid Particle Swarm,which Enables Indoor Path Planning Of UAVs

With the rapid development of science and technology,UAV has become an important part of all walks of life.They can complete more complex tasks in extreme environment and even replace human beings.Therefore,UAV has been widely used in various special fields.Since the beginning of the 21st century,more and more attention has been paid to advanced intelligent algorithms based on bionics principles,especially particle swarm optimization algorithm,whose advantages are extremely low computational parameters and can realize efficient flight path optimization,which makes it an important research direction in UAV path planning and other application fields,and has a huge development prospect.Through the in-depth study and improvement of the traditional algorithms,we find that they have a good application prospect in the interior space of the quadcopter.Compared with traditional path planning algorithms,particle swarm intelligent optimization algorithm has fewer parameters and higher robustness,making its operation more convenient and efficient.With the development of technology,intelligent optimization algorithms have made great progress,and they have become powerful tools to solve practical problems and improve performance.This paper discusses in depth the application of hybrid particle swarm optimization algorithm combining GA algorithm and PSO algorithm in indoor path planning of four-rotor UAV.They are two commonly used swarm intelligent optimization algorithms,which can effectively improve the efficiency and accuracy of path planning.In this paper,we use Lidar sensors to collect map data and create a two-dimensional raster map model.The commonly used positioning algorithms are introduced.When discussing the path planning of the quadrotor UAV in indoor environment,some restrictive conditions are used to convert the actual situation into a mathematical model.Meanwhile,the optimized mixed particle swarm optimization algorithm is used to carry out indoor path planning simulation experiment,and the performance after optimization is improved by 3.05% compared with before optimization.Stability improved by 29.70%.Of.By using a P600 quadrotor UAV,we built an indoor UAV path planning experiment platform to explore its feasibility and operability in an indoor environment.We will conduct a series of indoor flight navigation tests to ensure safe flight.In this paper,we propose a new hybrid particle swarm optimization algorithm to help drones fly indoors.Through practical application of this algorithm,we have proved that it is effective.