Indonesian Journal of Electrical Engineering and Computer Science Vol. 29, No. 2, February 2023, pp. 1192∼1200 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v29.i2.pp1192-1200 ❒ 1192 Intelligent UAV path planning framework using artificial neural network and artificial potential field Meena Thangaraj, Ravi Sankar Sangam School of Computer Science andEngineering, VIT-AP University, Vijayawada, India Article Info Article history: Received Jul 5, 2022 Revised Oct 21, 2022 Accepted Nov 3, 2022 Keywords: Artificial neural network Artificial potential field Path planning Unmanned aerial vehicle 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 au- tonomous 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 ob- stacles 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. This is an open access article under the CC BY-SA license. Corresponding Author: Meena Thangaraj School of Computer Science and Engineering, VIT-AP University Vijayawada, Andhra Pradesh-522237, India Email: meenait3110@gmail.com 1. INTRODUCTION In current decade growth of unmanned aerial vehicles (UAV) is vast and it holds considerable amount of contribution in the worldwide economy. Application areas of UAVs concentratedin various fields related to business, utilization and governments purposes too. UAVs are also employed for industry based applications [1]. Early stage detection of forest fire can be performed by operating UAVs where green environment is saved. UAV path planning is required like these scenarios in which heights of the trees and altitude level of flight way also matters. Because UAVs utilize lower level than higher level altitude. UAV path planning ensures the safe flight way by avoiding the obstacles. They involve in various operations like rescue jobs, observing, launching missiles, safeguarding environment, communication and delivery [2], [3]. Methods implemented for UAV path planning play important role in concerning the degree of auton- omy [4]. Optimal path detection is the ultimate aim of UAV path planning process with in minimum period of time by ensuring safety measures [5]. Multiple research works are carried in UAV path planning research area for obtaining optimal path without collision like A* algorithm, D* Algorithm, visibility graph, random tree, dijkstras algorithm, probabilistic roadmap and so on [6]. Optimal decision making will provide remedy for the Journal homepage: http://ijeecs.iaescore.com