Path planning for indoor UAV based on Ant Colony Optimization

Abstract

UAV autonomous navigation is very useful in many applications, and path planning is one of the key technologies for UAV autonomous navigation. In this paper, the path planning problem to find the optimal path from the start location to the destination in an indoor environment is studied based on Ant Colony Optimization (ACO) algorithm. The workspace of UAV is modeled by applying the grid method, which is usually used in ground robot planning. With the help of 3D gird and new climbing weight parameter, a modified algorithm is presented to solve the premature convergence and low efficiency problem of traditional Ant Colony Optimization algorithm. The simulation results show that these improvements make the search of the optimal path rapidly and efficiently.