Advancements in Drone Navigation: A Comprehensive Survey on ML-Based NavICNavigation Systems

Abstract

This paper presents a comprehensive overview of recent advancements in battery life vaticination and operation systems, with operations gauging IoT bias, drones, and UAVs. The exploration encompasses a DNN- grounded frame for real- time detector networks, achieving a notable 90 delicacy and icing secure prognostications through blockchain technology. also, it explores the complications of defining lithium- ion battery life, emphasizing factors similar as cycle number and electrochemical responses. The critical review of online battery continuance vaticination styles underscores the eventuality of deep literacy models. likewise, the study delves into a data- driven approach for prognosticating drone battery discharge, demonstrating its real- world connection through line segmentation. The development of a machine literacy- grounded Battery Management System for UAVs integrates DNN and LSTM for state- of- charge retrogression and RF for state- of- health bracket. Hotspots in UAV Battery Management Systems are bandied, emphasizing vaticination styles for state- of- charge and state- of- health. The paper introduces a mongrel algorithm for IoT network battery life vaticination, incorporating Random Forest and PCA, while also exploring energy- saving mechanisms.
The discussion extends to the use of mobile networks for drone control, emphasizing BVLOS operations and addressing evolving regulatory landscapes. The integration of UAVs into wireless networks is explored, tackling challenges in disaster relief, surveillance, and precision agriculture. Secure Web Socket-basedarchitectures for connecting IoT devices to web applications are investigated. The paper also examines the remote control of ROS drones using the Web Socket protocol and the Django devel- opment environment, emphasizing security considerations. The introduction of RAMWS in mobile cloud computing enhances communication reliability between mobile devices and the cloud. The discussion extends to multi-UAV remote control systems, leveraging intelligent devices for versatile control interfaces. Lastly, the focus on UAV communication and networking tech- nologies explores mesh networks, cellular communications, and the integration of 5G for real-time applications. Overall, these research endeavors collectively contribute to the advancement of battery prediction, UAV technology, and communication frame- works, addressing challenges and enhancing efficiency across diverse applications.