Research on UAV path planning based on improved particle swarm algorithm with inertia weight

For standard particle swarm algorithm in track planning, there are search depth convergence slow, local search ability is weak shortcomings, tthis paper studies a based on the inertial weight of nonlinear improvement strategy particle swarm algorithm for drone path planning, using logarithmic function to control inertial weight change, and add Gaussian random adjustment in the process of evolution to adjust the inertia weight, enhance the search ability of the algorithm, to enhance the stability of the path planning, improve the ability to get better path solution.