Task Assignment and Path Planning of Multiple Unmanned Aerial Vehicles Using Integer Linear Programming

Abstract

In this paper, we propose a strategy to enhance the performance of task assignment and path planning in applications of distributed multiple unmanned aerial vehicles (multi-UAV). Multi-UAVs are made up of many small UAVs with limited mission resources. They can operate autonomously, appropriately, and universally. UAV swarm task coordination and resource allocation can be achieved with reasonable allocation of UAV tasks and resources according to the UAV region and its own performance. Based on the in-depth research of the traditional auction algorithms, this work proposes a method that can improve multi-UAV task allocation efficiency, namely the Integer Linear Programming (ILP). We are proposing ILP formulation for numerous drones and tasks in an environment that enables the completion of tasks. We also measure the computational time requirements of the proposed ILP approach with various number of drones and tasks, so that the limit of this approach can be identified for practical application. This research overcomes the difficulties between task allocation and path planning. Compared with auction-based algorithms this technique can better complete allocation results and reduce resource consumption. It's expected that the simulation results will show that the algorithm is effective in computing and task execution efficiency.