Drone System Research Articles

Real-Time Object Localization Using a Fuzzy Controller for a Vision-Based Drone

This study proposes a drone system with visual identification and tracking capabilities to address the issue of limited communication bandwidth for drones. This system can lock onto a target during flight and transmit its simple features to the ground station, thereby reducing communication bandwidth demands. RealFlight is used as the simulation environment to validate the proposed drone algorithm. The core components of the system include DeepSORT and MobileNet lightweight models for target tracking. The designed fuzzy controller enables the system to adjust the drone’s motors, gradually moving the locked target to the center of the frame and maintaining continuous tracking. Additionally, this study introduces channel and spatial reliability tracking (CSRT) switching from multi-object to single-object tracking and multithreading technology to enhance the system’s execution speed. The experimental results demonstrate that the system can accurately adjust the target to the frame’s center within approximately 1.5 s, maintaining precision within ±0.5 degrees. On the Jetson Xavier NX embedded platform, the average frame rate (FPS) for the multi-object tracker was only 1.37, with a standard deviation of 1.05. In contrast, the single-object tracker CSRT exhibited a significant improvement, achieving an average FPS of 9.77 with a standard deviation of 1.86. This study provides an effective solution for visual tracking in drone systems that is efficient and conserves communication bandwidth. The validation of the embedded platform highlighted its practicality and performance.

Introduction of AI and Drone System in Field of Farming

The integration of artificial intelligence (AI) and drone systems in the field of cultivating addresses a groundbreaking way to deal with rural practices. This examination paper investigates the complex applications and ramifications of man-made intelligence and robot advancements in present day cultivating, zeroing in on their parts in upgrading efficiency, maintainability, and dynamic cycles. Through a far-reaching survey of existing writing, contextual investigations, and technological advancements the reconciliation of simulated intelligence and robot frameworks addresses a change in perspective in the horticultural business, enabling ranchers with significant experiences, upgrading asset usage, and cultivating feasible farming practices. By utilizing state of the art advancements and interdisciplinary methodologies, ranchers can upgrade efficiency, strength, and food security notwithstanding developing ecological and financial difficulties.

Development and Optimization of a Wireless Power Charging System for an Inspection Drone

Unmanned aerial vehicles (UAVs) have advanced and are now employed in a variety of industries, including aerial photography, the military, and transportation. Due to its low battery life, charging it can be difficult. A good UAV solution has been thought of as wireless charging. In this research, we suggested an inductively coupled wireless power transmission system (WPT) and examined the inductive coupling’s power efficiency. A portable energy receiving device positioned on the base of the UAV and a charging station with an induction coil array were both included in the WPT. Additionally, the WPT equivalent circuit was used to examine power efficiency, and a prototype model of the battery coupled with a quadcopter for testing power efficiency was developed. The results onboard the charging circuit design contributed with polarity modulation and safety charging the battery pack, with very light components that considerably decrease the size and weight as much as possible. The charging power conversion efficiency has achieved 83.76% at the maximum.

Autonomous Drone System for Emergency Charging of Electric Vehicles

This study explores the development of an emergency charging system for electric vehicles (EVs) using drones. The system is designed to address the limitations of current EV charging infrastructure, particularly in remote or inaccessible areas. The proposed solution involves a drone equipped with a battery pack, which can be dispatched to a stranded EV to provide immediate power.

A Search and Detection Autonomous Drone System: From Design to Implementation

A Search and Detection Autonomous Drone System: From Design to Implementation

Reinforcement Learning-Based Counter Fixed-Wing Drone System Using GNSS Deception

Reinforcement Learning-Based Counter Fixed-Wing Drone System Using GNSS Deception

ML-based Intrusion Detection for Drone IoT Security

The integration of drones into various industries brings about cybersecurity challenges due to their reliance on internet connectivity. To address this, we propose a comprehensive cybersecurity architecture leveraging machine learning (ML) algorithms and Internet of Things (IoT) technologies within the Internet of Drones (IoD) framework. Our architecture employs IoT-enabled sensors strategically placed across the drone ecosystem to collect and analyze data on system behaviors, communication patterns, and environmental variables. This data is then processed by a centralized platform equipped with sophisticated ML algorithms for pattern identification and anomaly detection. A key feature is the dynamic learning mechanism, enabling real-time intrusion detection by adapting to evolving threats. By combining IoT and ML, the system proactively defends against cyberattacks by distinguishing between typical and abnormal activity. Emphasis is placed on data integrity and confidentiality through secure communication protocols and cryptographic algorithms. Extensive simulations and tests validate the framework's effectiveness in various IoD scenarios, demonstrating its ability to swiftly identify intrusions and informing future enhancements. This comprehensive study meticulously examines the pressing cybersecurity concerns within the burgeoning drone industry. It proposes a robust architectural framework designed to enhance security for drone-enabled applications in our increasingly interconnected world. By harnessing the synergies between Internet of Things (IoT) and Machine Learning (ML) technologies, this innovative approach aims to fortify the integrity and reliability of drone systems.

Power Management Strategy of Hybrid Fuel Cell Drones for Flight Performance Improvement Based on Various Algorithms

In recent years, there has been a growing demand for multipurpose drones that can handle surveillance, environmental monitoring, and urgent deliveries. This trend has spurred the need for increased power and longer flight times for drones. Hence, many researchers introduced various hybrid systems to enhance endurance. In particular, a hybrid system that integrates solar cells, fuel cells, and batteries can substantially enhance a drone’s endurance. However, linking multiple power sources necessitates a control strategy that prioritizes safety and durability. It is also essential to analyze the control characteristics of each component as the dynamic behavior of individual components, coupled with environmental factors, significantly impacts the overall dynamic characteristics of drone systems. This study introduces a PEMFC–battery drone model. The model’s dynamic characteristics can be evaluated based on changes in environmental conditions and the control strategies of primary components. The validity of this model is confirmed by analyzing the dynamic characteristics of drone systems. As a result, the MRAC logic applied to the flight-level control and thrust motor of the drone was found to amplify the characteristics of the underlying PI and IP controllers. These control characteristics can lead to the development of control strategies for improving the flight performance or power durability of the aircraft by being properly applied to the flight environment of the drone.

Development of Adaptive Control System for Aerial Vehicles

This article represents and compares two control systems for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV): a sliding proportional–integral–derivative (PID) controller and an adaptive L1 controller. The goal is to design a high-performing and stable control system for a specific VTOL drone. The mathematical model of the unique VTOL drone is presented as a control object. The sliding PID and adaptive L1 controllers are then developed and simulated, and their performance is compared. Simulation results demonstrate that both control systems achieve stable and accurate flight of the VTOL drone, but the adaptive L1 controller outperforms the sliding PID controller in terms of robustness and adaptation to changing conditions. This research contributes to ongoing work on adaptive control systems for VTOL UAVs and highlights the potential benefits of using L1 adaptive control for this application.

An Efficient Optimization of Battery-Drone-Based Transportation Systems for Monitoring Solar Power Plant

Nowadays, developing environmental solutions to ensure the preservation and sustainability of natural resources is one of the core research topics for providing a better life quality. Using renewable energy sources, such as solar energy, is one of the solutions that can reduce the overuse of natural resources. This research aims to boost the efficiency of solar energy plants by proposing a novel approach to optimize the total flying time of battery-based drone systems to enhance the performance of solar plant systems. The contribution of the proposed approach is to solve scheduling problems based on timing constraints to monitor the solar plant. The main objective of the proposed approach is to maximize the drone’s minimum total flying time, which will increase the availability and reliability of the solar plant monitoring system. Time to empty values is calculated based on battery degradation rates. This problem is proven to be NP-hard. Four categories of enhanced algorithms were developed to solve drones’ scheduling problems in handling various tasks within multiple errands in the extent of solar parks in the monitored power plant to achieve the desired objective. Experimental results of the presented algorithms showed that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M2S$</tex-math> </inline-formula> algorithm has a stable performance behavior in all conducted experiments.

Rotating 3D laser mapping system for multi-rotor drones

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

Enhancing Autonomous Drones With Self- Designed Payload Dropping Mechanism

This research paper provides a comprehensive exploration of an autonomous drone experiment integrating the Pixhawk 2.4.8 flight controller. The study offers a detailed analysis of a drone system characterized by a 200-gram payload capacity and a maximum flight duration of 13 minutes. The CAD model, meticulously designed using Dassault's SolidWorks Design Tool, incorporates carbon fiber propellers to optimize both durability and flexibility. One noteworthy contribution of this research is the introduction of a self-designed payload deployment mechanism, further extending the drone's capabilities. Additionally, specialized landing equipment is developed to enhance takeoff and landing stability, thereby elevating overall system performance. The study emphasizes the drone's precise control and navigation capabilities, made possible by its robust processing capacity and integrated sensors. Leveraging the computational prowess of the Raspberry Pi 4B, this project efficiently manages complex algorithms crucial for autonomous operations, all while maintaining cost-effectiveness. The Raspberry Pi's high-resolution capabilities and seamless integration prove to be the optimal choice for accurate object detection, environmental recognition, autonomous navigation, and successful payload deployment missions. This research contributes valuable insights into the integration of advanced technologies in autonomous drone systems, with a particular focus on enhancing their capabilities through self-designed payload deployment mechanisms.

The Operation Method of Hybrid Power Supply System Combining Lithium Polymer Battery and Supercapacitor for Industrial Drones

This paper proposes a hybrid power supply system for commercial drones. The proposed hybrid power supply system consists of a lithium polymer battery, a supercapacitor, and a power converter for charging the supercapacitor. In the proposed system, the supercapacitor is pre-charged with a lithium polymer battery through a power converter, and the supercapacitor first supplies the power required for the drone’s initial startup and lift-up. Afterward, in the section where the power consumption of the drone is low, the battery and the supercapacitor supply power together, minimizing the stress on the battery. To verify the proposed hybrid power supply system, a computer simulation was conducted, the actual hardware was fabricated, and experiments were performed.

AI-Based Drone System for Medical Support in Congested Areas

Serving the needs of human beings is much more important to us. So, considering how to service the product or provide medicinal support is also very important to reach the destination. While supported by the local field-side forces, it is going through delays and getting affected by the delays in medical treatment. Why this failure in medical support? To avoid this delay, I have come up with a trend in technology in real life to send things like oxygen to the nearby hospital and make it emergency support on an urgent basis. At the same time, we can look for college and industry, food, and grocery items that we can utilize for the same methods. We can make it using AI technology used in the drone system and so by using different scales of sensors and cameras for recognition for bill service for auto-detection and bill payment as well. Nowadays, most people try to use their outside food purchases quickly, so we can get it via air drone easily without delay. This process is unintentionally free and GPS-based, with an advantage system to track the location. And confirmation of customer detection can help to unlock the lock-unlock the drone's locked body, get material, and lock the door once service is received. If there is any unwanted attack, it will be updated with the tracking system. There is an anti-drone system used here. This activity can be avoided.&#x0D;

Digital Forensic Research for Analyzing Drone Pilot: Focusing on DJI Remote Controller.

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as 'Mobile IoT' or 'Flying IoT', are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot's account information, the specific drone used, pairing timestamps, the pilot's operational location, the drone's flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations.

An airborne object detection and location system based on deep inference

In recent years, with the development of sensors, communication networks, and deep learning, drones have been widely used in the field of object detection, tracking, and positioning. However, there are inefficient task execution and some complex algorithms still need to rely on large servers, which is intolerable in rescue and traffic scheduling tasks. Designing fast algorithms that can run on the airborne computer can effectively solve the problem. In this paper, an object detection and location system for drones is proposed. We combine the improved object detection algorithm ST-YOLO based on YOLOX and Swin Transformer with the visual positioning algorithm and deploy it on the airborne end by using TensorRT to realize the detection and location of objects during the flight of the drone. Field experiments show that the established system and algorithm are effective.

Pattern Orientation Finder (POF): A Robust, Bio-Inspired Light Algorithm for Pattern Orientation Measurement

We present the Pattern Orientation Finder (POF), an innovative, bio-inspired algorithm for measuring the orientation of patterns of parallel elements. The POF was developed to obtain an autonomous navigation system for drones inspecting vegetable cultivations. The main challenge was to obtain an accurate and reliable measurement of orientation despite the high level of noise that characterizes aerial views of vegetable crops. The POF algorithm is computationally light and operable on embedded systems. We assessed the performance of the POF algorithm using images of different cultivation types. The outcomes were examined in light of the accuracy and reliability of the measurement; special attention was paid to the relationship between performance and parameterization. The results show that the POF guarantees excellent performance, even in more challenging conditions. The POF shows high reliability and robustness, even in high-noise contexts. Finally, tests on images from different sectors suggest that the POF has excellent potential for application to other fields as well.

An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares

Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.

Geographic-Region Monitoring by Drones in Adversarial Environments

We consider surveillance of a geographic region by a collaborative system of drones. The drones assist each other in identifying and managing activities of interest on the ground. We also consider an adversary who can create both genuine and fake activities on the ground. The objective of the adversary is to use fake activities to maximize the response time to genuine activities. We present two collaboration algorithms and analyze their response times, as well as the adversary’s efforts in terms of the number of fake activities required to achieve a certain response time.

Delivery network design of a locker-drone delivery system

ABSTRACT Drones are increasingly used for last-mile delivery due to their speed and cost-effectiveness. This study focuses on a novel locker-drone delivery system, where trucks transport parcels from the warehouse to lockers, and drones complete the final delivery. This system is ideal for community and intra-facility logistics. The research optimises the network design by determining the location of lockers, the number of drones at each locker, and the assignment of demands to lockers, minimising operating costs. Both single-parcel and multi-parcel capacity drones are examined. We build an optimisation model for each system, considering drone service capacity as a critical constraint. We design an algorithm combining average sample approximation and a genetic algorithm to address demand uncertainty. The algorithm's efficiency is validated through comparative analysis with Gurobi. Numerical experiments, using real and generated data, optimise the network design. Results show that the multi-capacity drone system requires fewer lockers and drones than the single-capacity system. Although the single-capacity system yields lower drone delivery costs, it incurs higher truck delivery costs. Additionally, a comprehensive cost analysis compares the cost-efficiency of the locker-drone system with a conventional drone delivery system, revealing the cost-saving advantage of the locker-drone system.