Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. By relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches.
@article{arxiv.2310.06329,
title = {Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using Object Detection Algorithms},
author = {Aditya Vadduri and Anagh Benjwal and Abhishek Pai and Elkan Quadros and Aniruddh Kammar and Prajwal Uday},
journal= {arXiv preprint arXiv:2310.06329},
year = {2023}
}
Comments
Second International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS 2023)