Optimal trajectory UAV path design based on bezier curves with multi-hop cluster selection in wireless networks

In the past decade, multi-UAV systems have gained significant attention and companies from in various fields. The main aims of growing such systems are able to operate in a coordinated way in complicated situations. To encompass and increase the speed of the applications, this research proposed optimization with intelligence. In the existing work, it does not detect the collision effectively. In this work, the Particle Swarm Optimization algorithm based on fast matching square is proposed for flightpath planning in Multi-UAVs is used. A distributed full coverage optimal route design is derived using the particle swarm optimization (PSO) technique, and a trajectory planner is built utilizing a dynamic fitness function. We applied the PSO technique for every UAV individually to achieve active fitness, maximizing the fitness function while reducing the cost function. The novelty of this work is using the dynamic fitness function for PSO while selecting optimal data. Fast matching square FM(2) helps to detect the collision and avoid collisions between the paths. The suggested method generates an optimal path for multi-UAVs and updates the maps for swarms of UAVs. According to experimental findings, the suggested PSO-FM(2) outperforms the previous FOA in aspects of convergence and accuracy. Finally, the proposed method is compared with existing algorithms such as MSFOA and PSO.