A Quantitative Assessment Framework for UAV Hardware Components

A Strategic Framework for Integrating Unmanned Aerial Vehicles (UAV)-Based Pavement Assessments into the Egyptian Road Network

Pavement condition assessment is essential for effective road network management, as paved roads deteriorate over time due to traffic loading and environmental effects. Traditional pavement surveys rely on in-situ measurements and visual inspections to identify surface distresses such as cracking, raveling, and weathering. Although widely used, these methods are often labour-intensive, time-consuming, costly, and may disrupt traffic while exposing inspectors to safety risks. Recent advances in unmanned aerial systems (UAS) provide a promising alternative for pavement condition assessment. UAV-based surveys enable rapid data collection over large areas using high-resolution imaging and sensor technologies, which can be integrated with artificial intelligence (AI) techniques for automated pavement distress detection and analysis. In Egypt, the rapid expansion of the road network and increasing maintenance demands highlight the need for an efficient, continuous, and reliable pavement monitoring system. This study presents an Egypt-focused framework that links UAV data acquisition, AI-based distress detection, and PCI-based decision-making to support the integration of UAV-based pavement inspection into existing road management practices. This study supports an Egypt-focused framework for integrating UAV-based pavement inspection into existing road management practices. The proposed framework outlines UAV data acquisition, AI-based distress detection, and pavement condition evaluation workflows, while considering local environmental, operational, and regulatory constraints. The framework is informed by successful international applications and is intended to enable safer, faster, and more cost-effective pavement assessment to support sustainable road network management in Egypt.

Research on Structural Improvements of UAVs under Extreme Weather Conditions

UAV-enabled non-destructive testing for robotic infrastructure inspection: technologies, challenges and future directions

Advancements in UAV-Based Integrated Sensing and Communication: A Comprehensive Survey

This survey provides a systematic review across six interconnected domains—channel estimation (CE) and beam tracking, throughput maximization, weighted sum rate (WSR) and sensing co-optimization, delay and age of information (AoI) minimization, energy efficiency (EE), and PLS—each supported by a structured comparative table covering over 80 methodologies.

AERONAUTICAL DRONES, PART 2: CURRENT APPLICATIONS AND TRENDS

This paper examines the current applications and emerging development trends of unmanned aerial vehicles (UAVs), with emphasis on the technological and operational evolution of contemporary drone systems across civil and military domains. The study reviews the expanding use of UAVs in precision agriculture, infrastructure inspection, logistics, emergency response, urban air mobility, intelligence, surveillance and reconnaissance (ISR), electronic warfare and tactical operations, highlighting the operational advantages offered by unmanned platforms in diverse mission environments. The principal challenges affecting future UAV deployment are also analyzed, including battery endurance, cybersecurity, communication reliability, regulatory compliance, airspace integration and environmental constraints. Attention is given also to the increasing role of artificial intelligence, autonomous mission execution, swarm coordination, advanced propulsion technologies and digital airspace management in shaping the next generation of unmanned aircraft systems. Current research directions indicate a transition from individually operated drones toward interconnected, intelligent and highly autonomous aerial systems capable of cooperative decision- making and large-scale deployment. The analysis shows that future progress in unmanned aviation will depend not only on improvements in aircraft performance but also on advances in systems integration, secure communications, resilient autonomy and regulatory frameworks that enable the safe and efficient operation of increasingly complex UAV ecosystems.

Design and Validation of an IoT-Enabled Safety Monitoring System for Agricultural UAV Operations

The challenges faced by Unmanned Aerial Vehicles (UAVs) used in precision agriculture are growing. Operational risks arise from harsh conditions, such as abnormal flight behaviors, extended missions, and environmental circumstances. Current flight controllers primarily focus on navigation and stabilization, with only limited ability to monitor real-time safety. This study presents a low-cost, real-time safety monitoring platform for agricultural UAVs using an ESP32 microcontroller integrated with an MPU6050 inertial measurement unit (IMU) and a DHT11 temperature sensor. The system continuously measures roll, pitch, and on-board temperature, sending telemetry data to a cloud-based IoT platform for visualization and alerts. An empirically derived, threshold-based anomaly detection plan, based on over 100 normal flight data points, more than 20 Pixhawk crash log analyses, and observations of climatic conditions from 28 Indian states, allows for timely detection of abnormal operating conditions. The monitoring module has a sampling rate of 30-40 Hz and uses persistence-based detection logic to ensure that sustained anomalies are present over a 125-165 ms timespan. It was experimentally verified that roll and pitch estimates based on an atan2-based approach have a mean absolute error of 7.62° and 13.52°, respectively, compared to reference flight controller measurements. Temperature validation shows that the DHT11 sensor's temperature readings do not exceed the ±4.6°C deviation limit relative to the reference readings. These findings reveal that the suggested framework offers a scalable, cost-effective, and easily implemented solution for safety monitoring and early detection of abnormal flight conditions in agricultural UAV delivery.