An improved ant colony optimization for path planning with multiple UAVs
Jing Li, Yonghua Xiong, Jinhua She
Abstract
As exploiting unmanned aerial vehicles (UAVs) as mobile elements is a new research trend recently, approximation algorithms to solve path planning problems for UAVs are promising approaches. This paper present a solution for the problem of minimum mission time to cover a set of target points in the surveillance area with multiple UAVs. In this methodology, we propose an improved ant colony optimization (ACO) combining ACO with greedy strategy. The main purpose is to find the optimal number of UAVs and to plan the paths of the minimum mission time. Simulation results demonstrate the validity and the superiority of the proposed algorithm.
Topics & Concepts
Ant colony optimization algorithmsComputer scienceMotion planningGreedy algorithmPath (computing)Set cover problemMathematical optimizationCover (algebra)Set (abstract data type)Travelling salesman problemPlan (archaeology)Artificial intelligenceRobotAlgorithmEngineeringMathematicsGeographyMechanical engineeringArchaeologyProgramming languageRobotic Path Planning AlgorithmsUAV Applications and OptimizationVehicle Routing Optimization Methods