Multi Colony Ant Optimization for UAV Path Planning with Obstacle Avoidance

Abstract

In recent years, the availability of low-cost and autonomous unmanned aerial vehicles (UAVs) results in the use of them for different types of military and commercial applications. The crucial part of the autonomous UAVs is their online or offline path planning algorithms. In the literature, there are many types of solutions, which use evolutionary and/or swarm intelligence approaches. Ant colony optimization is one of the mostly used algorithms, which has been applied to solve different type of path planning problems. Mainly, most of these studies have focused on a single colony ant colony optimization (ACO), which can find better solutions in fewer computation times. However, it is able to converge to a sub-optimal solution in the planning process. One approach to avoid the premature convergence is the use of Multi-Colony ACO, in which a number of ant colonies try to find an optimal solution cooperatively by exchanging their valuable information with each other. In this paper, it is aimed to implement an obstacle avoidance UAV path planning by using Multi-Colony ACO algorithm. We experimentally investigate the use of Multi-Colony ACO approach results from an effective path planning for UAVs with a comparison to a single colony ACO approach.