Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram‐Tabu Genetic Algorithm

Abstract

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

UAV obstacle avoidance with PID control based on improved sparrow search algorithm

“…Static global path planning is typically realized through conventional optimization algorithms such as node-based A* algorithm [4] and Dijkstra's algorithm [5], which are popular for finding the shortest path to a target node but may consume substantial time. Additionally, sampling-based approaches like Voronoi diagrams [6] and probabilistic roadmap [7] require scene sampling before path searching. Traditional optimization algorithms are generally applicable to static scenes or scenarios with pre-scanned offline models but exhibit reduced reliability in addressing complex scenarios [8].…”

Improved particle swarm optimization based on multi-strategy fusion for UAV path planning

“…In recent years, a variety of methods have been proposed by domestic and foreign scholars for the UAV path planning, which are mainly divided into classical algorithms and meta-heuristic algorithms. Classical algorithms include probabilistic roadmap (PRM) (Jin et al ., 2023), rapid-exploration random tree(RRT) (Yang et al ., 2022), Voronoi diagram (Tan et al ., 2021), A* algorithm (Farid et al ., 2022), artificial potential field method (Jayaweera and Hanoun, 2020), etc. Meta-heuristic algorithms include genetic algorithm (GA) (Jamshidi et al ., 2022), differential evolution (EA) (Yu et al ., 2020), grey wolf optimization(GWO) (Zhang et al ., 2022), artificial bee colony (ABC) (Ebrahimnejad et al ., 2021), ant colony optimization(ACO) (Di Caprio et al ., 2022), particle swarm optimization(PSO) (Fu and Hu, 2021; Zhao et al ., 2021; Wang and Wang, 2020), etc.…”