Abstract
Collision-free distributed path planning for the swarm of unmanned aerial vehicles (UAVs) in a stochastic and dynamic environment is an emerging and challenging subject for research in the field of a communication system. Monitoring the methods and approaches for multi-UAVs with full area surveillance is needed in both military and civilian applications, in order to protect human beings and infrastructure, as well as their social security. To perform the path planning for multiple unmanned aerial vehicles, we propose a trajectory planner based on Particle Swarm Optimization (PSO) algorithm to derive a distributed full coverage optimal path planning, and a trajectory planner is developed using a dynamic fitness function. In this paper, to obtain dynamic fitness, we implemented the PSO algorithm independently in each UAV, by maximizing the fitness function and minimizing the cost function. Simulation results show that the proposed distributed path planning algorithm generates feasible optimal trajectories and update maps for the swarm of UAVs to surveil the entire area of interest.
Highlights
Unmanned Aerial Vehicles (UAVs), known as drones, gained much popularity in the area of surveillance due to their capability in vertical take-off and landing, and high maneuverability, which provides various benefits in various platforms or environments
We proposed a distributed 3-D path planning for multiple unmanned aerial vehicles (UAVs), based on Particle Swarm Optimization with Bresenham Algorithm, to make an optimal trajectory for multiple UAVs
We obtained the optimal weight of Surveillance Area Important (SAI) for an objective value to obtain dynamic fitness to generate a collision-free trajectory for multiple UAVs
Summary
Unmanned Aerial Vehicles (UAVs), known as drones, gained much popularity in the area of surveillance due to their capability in vertical take-off and landing, and high maneuverability, which provides various benefits in various platforms or environments. UAVs are gaining more popularity in surveillance For this reason, path planning for UAVs is more crucial, and it plays a fundamental role in the autonomous flight system for unmanned aerial vehicles (UAVs). In our proposed 3-D path planning methodology, we used Particle Swarm Optimization (PSO), due to its benefits like the advantages of easy implementation, simple parameter settings, fast convergence speed, and for which the PSO algorithm has been widely used in various fields, such as, functions optimization, neural networks training, and fuzzy logic system control are notable. The authors in [30,31,32,33] discussed distributed path planning using the PSO algorithm and the designing of the quadrotor control
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