Path planning for multiple Unmanned Aerial Vehicles using genetic algorithms

In the future, autonomous Unmanned Aerial Vehicles (UAVs) need to work in teams to share information and coordinate activities. The private sector and government agencies have implemented UAVs for home-land security, reconnaissance, surveillance, data collection, urban planning, and geometrics engineering. Significant research is in progress to support the decision-making process for a Multi-Agent System (MAS) consisting of multiple UAVs. This paper investigates fundamental issues in path planning for multiple UAVs. MASs with multiple UAVs are typical distributed systems. We propose to use genetic algorithms to plan multiple paths for multiple UAVs. Simulation technologies have become important to the development of aerospace vehicles. In this research, we verify the proposed path planning approach using Matlab. Simulation results demonstrate that the proposed approach is able to plan multiple paths for UAVs successfully.