A Novel Technique for Drone Path Planning Based on a Neighborhood Dragonfly Algorithm

Autonomous aerial drone navigation is a rapidly growing topic of research due to its vast application in various indoor applications, including surveillance, search and rescue missions, and environmental monitoring. Current research focuses on the implementation of neighborhood dragonfly algorithms (NDAs) for path planning for single and multiple drones in various indoor environments containing stationary and moving obstacles. The collaborative behavior of dragonflies is a key concept in the current study that helps in exploring the solution space effectively and results in a faster convergence rate. To validate the performance of the proposed NDA approach, various environments are created in real time, and replicas of the same are generated using MATLAB software. Our analysis shows a close agreement between simulation and experimental results, with path length and navigational time differences of less than 5.7%. This underscores the consistency and feasibility of the NDA approach, placing the groundwork for robust and efficient drone navigation systems. The proposed NDA approach is also compared with those already developed, like IACO and PRM, in a similar environment. The NDA approach shows a better performance in terms of smooth path planning and path length optimization. The saving in path length is more than 5%.