Intelligent UAV path planning framework using artificial neural network and artificial potential field

Abstract

Unmanned aerial vehicles (UAVs) are utilized extensively in various fields of daily activities in the day to day life and industrial applications. The raises of utilization of UAVs guide the researchers to concentrate on various problems like handling rich and large-scale information and uninterrupted communication. Further, to achieve the above the obstacle free zone is mandatory and the present autonomous drones may fail to handle such situations. To address the mentioned issues, an effective path planning algorithm is needed, to find the optimal path and obstacle free mobility. Hence, UAV path planning needs intelligent and autonomous navigation system by providing high level of optimization in order to attain optimal path with the obstacles avoidance. In this paper, AI employed framework for UAV path planning is proposed by utilizing the salient features of both artificial neural network (ANN) and artificial potential field (APF). ANN is implemented for obtaining optimal path and APF is utilized for evading the obstacles throughout the path. Further, the implementation results show the better performance than the existing works in terms of the collision free optimal path for UAVs.