A Review on Autonomous Drone : Object Detection And Avoidance

Author(s): Kalrav Gediya, Pooja Bhatt, Swati Sharma

Publication #: 2410001

Date of Publication: 04.10.2024

Country: India

Pages: 1-7

Published In: Volume 10 Issue 5 October-2024

Abstract

Autonomous drones have become critical in modern

applications such as surveillance, search and rescue, and

logistics. A key challenge to their deployment lies in

ensuring safe navigation through object detection and

collision avoidance. This review presents an in-depth

analysis of recent advancements in autonomous drone

object detection and avoidance systems, focusing on

machine learning techniques, particularly deep learning,

and their integration with sensor data for real-time

decision-making. The review synthesizes findings from

25 significant studies published between 2020 and 2023,

covering both algorithmic developments and sensor

technologies. Key developments include the use of

convolutional neural networks (CNNs), reinforcement

learning (RL), and hybrid sensor systems that enhance

obstacle detection and path planning. The paper

highlights current limitations such as computational

constraints, small object detection, and real-time

processing challenges. Finally, the review explores

emerging trends such as 3D object detection and the role

of 6G networks in enhancing UAV(Unmanned Aerial

Vehicle) communication for collision avoidance. This

comprehensive review serves as a foundation for further

research, emphasizing the potential of AI-driven UAVs

in complex, dynamic environments.

Keywords: Drone, UAV (Unmanned Aerial Vehicle), CNN (convolutional neural networks), Reinforcement Learning, Sensors, Object Detection, Deep Learning

Download/View Paper's PDF

Download/View Count: 270

Share this Article