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The current disruptive innovation in civilian drone (UAV) applications has led to an increased need for research and development in UAV technology. The key challenges currently being addressed are related to UAV platform properties such as functionality, reliability, fault tolerance, and endurance, which are all tightly linked to the UAV flight con...
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This paper presents an analysis of a Total Energy Control System (TECS) introduced by Lambregts to control unmanned aerial vehicle (UAV) velocity and altitude by using the total energy distribution. Furthermore, an extended Kalman filter (EKF) approach was used to predict aircraft response in terms of angular rates and linear acceleration during a...
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... Bromine is a naturally occurring element that is often found in seawater and freshwater (Ariza et al., 2021). Fluoride is a naturally occurring mineral found in groundwater and natural springs (Ebeid et al., 2018). Sulphate, similarly, found in mineral-rich groundwater, high levels of sulfate may cause laxative effects, resulting in diarrhea and dehydration. ...
Physical sampling of water on site is necessary for various applications like drinking water quality checking in lakes and checking for contaminants in freshwater systems. The use of water surface vehicles is a promising technology for monitoring and sampling water bodies, and they offer several advantages over traditional monitoring methods. This project involved designing and integrating a drone controller, water collection sampling contraption unit, and a surveillance camera system into a water surface vehicle (WSV). The drone controller unit is used to operate the boat from the starting location until the location of interest and then back to the starting location. The drone controller has an autopilot system where the operator can set a course and be able to travel following the path set, whereas the WSV will fight the external forces to keep itself in the right position. The water collection sampling unit is mounted onto WSV so when it travels to the location, it can start collecting and holding water samples until it returns to the start location. The field of view (FOV) surveillance camera helps the operator to observe the surrounding location during the operation. Experiments were conducted to determine the operational capabilities of the robot boat at the Ayer Keroh Lake. The water collection sampling contraption unit collected samples from 44 targeted areas of the lake. The comprehensive examination of 14 different water quality parameters were tested from the collected water samples provides insights into the factors influencing the pollution and observation of water bodies. The successful design and development of a water surface surveillance and pollution tracking vehicle marks the key achievements of this study. The developed collection and surveillance unit holds the potential for further refinement and integration onto various other platforms. They are offering valuable assistance in water body management, coastal surveillance, and pollution tracking. This system opens up new possibilities for comprehensive water body assessments, contributing to the advancement of sustainable and efficient water testing. Through careful sampling efforts, a thorough overview of the substances presents in the water collected from Ayer Keroh Lake has been compiled. This in-depth analysis provides important insights into the lake’s current condition, offering valuable information about its ecological health.
... Ebeid et al. presented an examination of the accessible open-source elements for drones, suitable for research and development, is presented. This examination encompasses open-source components in hardware, software, and simulation for drone systems, highlighting their primary attributes [1]. Pütsep et al. have identified/classified the flight controllers for Nano, Micro and Mini Drones [2]. ...
Flight controllers play vital role in stability, performance and control operations of a drone throughout the flight. Inappropriate flight controller selection can jeopardize both the drone and its payload, making it imperative to address the meticulous choice of drone flight controllers for specific applications. This work delves into the behavior of various flight controllers when tested in a quadcopter configuration under consistent structural and environmental conditions. The research not only examines the selection of flight controllers but also explores the challenges faced by amateur drone operators during setup and flight, offering practical techniques to overcome them. Among the various array of flight controllers available in the market, namely KK2.1.5, QQ super thunder, APM, DJI NAZA Mlite, and Pixhawk, have been used in this experiment. Comprehensive experiments were conducted using identical drone configurations to elucidate the precise difficulties associated with each flight controller. The results revealed instances of suboptimal stability, communication issues, and troubleshooting errors for certain controllers. Notably, KK2.1.5 and QQ super thunder exhibited non-responsiveness when utilized in hexacopters and octacopters. Ultimately, this research underscores the NAZA flight controller’s suitability for novice pilots among the tested options. The insights presented herein serve as a valuable resource for researchers seeking to select the most appropriate flight controller based on their piloting proficiency and for resolving challenges encountered during drone operations.
... A combination of sensors is typically employed to enable effective performance and communication in UAVs operation. There are different types of flight controller variations with sensors that are specifically designed to meet the requirements of autonomous UAVs [18,19]. Among the variations used in UAVs, the most commonly used are inertial measurement units which consist of a 9DoF accelerometer, gyroscope, and magnetometer (compass) with a barometer for altitude estimation [20]. ...
In recent times, the integration of Unmanned Aerial Vehicles (UAVs) with Internet of Things (IoT) platforms has brought a new dimension of mobility, significantly enhancing the capabilities of complex sensor networks. This chapter focuses on the development of an autonomous quadcopter, tailored as a core component of an AI-driven sensing network in indoor settings. We explore the integration of various vision-based and laser sensors with an onboard computer, utilizing the Robot Operating System (ROS) and WiFi technology for real-time wireless data collection and communication. Our system achieves significant milestones in 3D mapping and localization, demonstrating the tracking camera T265 sensor’s superiority in localization and computational efficiency over traditional SLAM algorithms. Through rigorous testing and calibration, including initial flight trials, we highlight the vast potential of these autonomous systems within an IoT framework. Moreover, we delve into the edge computing capabilities of the onboard Jetson Nano processor, optimizing its performance for enhanced functionality. These optimizations led to full utilization of the processor’s capabilities, reducing the mapping time of a 3 m × 3 m area from 4 to 5 min to under 3 min, with improved accuracy and reduced feature drift. Utilizing advanced AI and computer vision techniques, we test and compare SLAM algorithms, further cementing the role of UAVs in revolutionizing IoT applications.
... INAV Multicopter is one of the most recent open-source drone firmwares [47]. Having the same ancestor, it is close to BetaFlight but has improved navigation functionalities and ground station software supporting mission planning [48]. INAV firmware is widely used for fixed-wing and vertical takeoff and landing (VTOL) aircraft [49,50]. ...
Industrial inspection is a well-known application area for unmanned aerial vehicles (UAVs), but are modern market-available drones fully suitable for inspections of larger-scale industrial facilities? This review summarizes the pros and cons of aerial large-scale facility inspection, distinguishing it from other inspection scenarios implemented with drones. Moreover, based on paper analysis and additionally performed experimental studies, it reveals specific issues related to modern commercial drone software and demonstrates that market-available UAVs (including DJI and Autel Robotics) more or less suffer from the same problems. The discovered issues include a Global Navigation Satellite System (GNSS) Real Time Kinematic (RTK) shift, an identification of multiple images captured from the same point, limitations of custom mission generation with external tools and mission length, an incorrect flight time prediction, an unpredictable time of reaching a waypoint with a small radius, deviation from the pre-planned route line between two waypoints, a high pitch angle during acceleration/deceleration, an automatic landing cancellation in a strong wind, and flight monitoring issues related to ground station software. Finally, on the basis of the paper review, we propose solutions to these issues, which helped us overcome them during the first autonomous inspection of a 2400 megawatts thermal power plant.
... However, there is a lack of standardization of flight controller architecture and the proprietary use of flight controllers on a drone platform. Therefore, the author presents a survey of the publicly available open-source drone platform element that can be used for research and development purposes, which includes the open-source software, hardware, and simulation platform (Ebeid et al. 2018). ...
Recently, machine learning has been very useful in solving diverse tasks with drones, such as autonomous navigation, visual surveillance, communication, disaster management, and agriculture. Among these machine learning, two representative paradigms have been widely utilized in such applications: supervised learning and reinforcement learning. Researchers prefer to use supervised learning, mostly based on convolutional neural networks, because of its robustness and ease of use but yet data labeling is laborious and time-consuming. On the other hand, when traditional reinforcement learning is combined with the deep neural network, it can be a very powerful tool to solve high-dimensional input problems such as image and video. Along with the fast development of reinforcement learning, many researchers utilize reinforcement learning in drone applications, and it often outperforms supervised learning. However, it usually requires the agent to explore the environment on a trial-and-error basis which is high cost and unrealistic in the real environment. Recent advances in simulated environments can allow an agent to learn by itself to overcome these drawbacks, although the gap between the real environment and the simulator has to be minimized in the end. In this sense, a realistic and reliable simulator is essential for reinforcement learning training. This paper investigates various drone simulators that work with diverse reinforcement learning architectures. The characteristics of the reinforcement learning-based drone simulators are analyzed and compared for the researchers who would like to employ them for their projects. Finally, we shed light on some challenges and potential directions for future drone simulators.
... Moreover, they do not allow mobile users, applicable UAV policies, and the definition of different application types, which may have different SLA requirements, such as maximum tolerable delay. In most of the cases, existing simulators focus on the flight controls, visualization and physics [6]. In [7], Shah et al. proposed AirSim which is built on Unreal Engine and focused on realistic flying simulation of aerial ve-hicles. ...
Air components, including UAVs, planes, balloons, and satellites have been widely utilized since the fixed capacity of ground infrastructure cannot meet the dynamic load of the users. However, since those air components should be coordinated in order to achieve the desired quality of service, several next-generation paradigms have been defined including air computing. Nevertheless, even though many studies and open research issues exist for air computing, there are limited test environments that cannot satisfy the performance evaluation requirements of the dynamic environment. Therefore, in this study, we introduce our discrete event simulator, AirCompSim, which fulfills an air computing environment considering dynamically changing requirements, loads, and capacities through its modular structure. To show its capabilities, a dynamic capacity enhancement scenario is used for investigating the effect of the number of users, UAVs, and requirements of different application types on the average task success rate, service time, and server utilization. The results demonstrate that AirCompSim can be used for experiments in air computing.
... However, it's worth noting that only a limited number of these platforms incorporate fault tolerance features to enhance reliability. Among these opensource flight controller platforms, the Pixhawk series stands out as one of the most widely recognized and favored choices within the UAV community [10]. Table 3 lists the various Open source controllers used in building UAV/drones. . ...
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques. We delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology.
... The typical software used to configure and control multirotor drones is as follows [233][234][235]: ...
This paper presents an overview on the state of the art in copter drones and their components. It starts by providing an introduction to unmanned aerial vehicles in general, describing their main types, and then shifts its focus mostly to multirotor drones as the most attractive for individual and research use. This paper analyzes various multirotor drone types, their construction, typical areas of implementation, and technology used underneath their construction. Finally, it looks at current challenges and future directions in drone system development, emerging technologies, and future research topics in the area. This paper concludes by highlighting some key challenges that need to be addressed before widespread adoption of drone technologies in everyday life can occur. By summarizing an up-to-date survey on the state of the art in copter drone technology, this paper will provide valuable insights into where this field is heading in terms of progress and innovation.
... Recent UAV flight test campaigns include [89][90][91][92][93][94][95][96][97][98][99], which also include flight testing with incremental type control laws in [100][101][102][103][104]. Subscale flight testing and system identification was performed in [105][106][107][108][109]. In [110], both system identification and control law design were performed for the longitudinal motion of a fixed wing UAV. Complete autopilot systems for UAV have been built or described in, for example, [111][112][113][114][115][116] including the PX4 flight stack used in this study. ...
An incremental differential proportional integral (iDPI) control law using eigenstructure assignment gain design is tested in flight on a subscale platform to validate its suitability for fixed-wing flight control. A kinematic relation for the aerodynamic side-slip angle rate is developed to apply a pseudo full state feedback. In order to perform the gain design and assessment, a plant model is estimated using flight test data from gyro, accelerometer, airspeed and surface deflection measurements during sine-sweep excitations. Transfer function models for the actuators and surface deflections are identified both in-flight and on the ground for several different actuators and control surfaces using hall sensor surface deflection measurements. The analysis reveals a large variation in bandwidth between the different types of servo motors. Flight test results are presented which demonstrates that the plant model estimates based on tests with good frequency excitation, high bandwidth actuators and surface deflection measurements can be used to reasonably predict the closed-loop dynamic behavior of the aircraft. The closed-loop flight test results of the iDPi control law show good performance and lays the groundwork for further development.
... In the experiment, an open source flight control board, Pixhawk, was used to control the proposed UAV system with manipulator (Ebeid et al., 2018;. The board has fourteen outputs, eight of which are main outputs and six are auxiliary outputs. ...
In this study, a model was proposed for performing remote anesthesia on animals using a quadrotor unmanned aerial vehicle (UAV). The proposed model integrates a manipulator into the UAV, and a mechanism system for throwing an anesthesia needle was mounted on this manipulator. A solid model of the entire proposed system was created, and a mathematical model of the system was developed. The validity of the proposed model was verified by examining the system parameters through simulation studies based on this mathematical model. Two different scenarios were constructed in the simulations. In the first scenario, the system locked on the target for a fixed position, while the second scenario was on a target that is in motion in all three dimensions. In both simulations, the UAV flew autonomously along a predetermined trajectory. The results obtained were presented graphically. In addition, the system for which a solid model was created was produced and preliminary tests were carried out in the outdoor environment. It has been shown that this proposed system can be used in a real environment.
