Optimal path planning for drones based on swarm intelligence algorithm

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning.