Multi-UAV control testbed for persistent UAV presence: ROS GPS waypoint tracking package and centralized task allocation capability

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

The paper presents the analysis on stability of digital control system for unmanned vehicle with numerical analysis. The objective of this study is mainly emphasized on the fulfillment of the advanced control techniques according to the fundamental concepts of digital control system approaches for unmanned aerial vehicle system design. The targeted unmanned aerial vehicle system was designed based on the simple construction under the idea of fixed wing flight system approaches. The stability analyses on unmanned aerial vehicle are vital role to enhance the real world applications. The background concepts on digital control system for stability analysis on dynamic control system like unmanned aerial vehicle system. The appropriate controller design for dynamic control system of unmanned aerial vehicle is vital role to analyze the accurate stability condition for reality. The implementation of numerical analysis on compensator design has been developed by using MATLAB. The physical parameters in these analyses are based on the experimental outcomes from the recent research works in dynamical system implementations. The mathematical approaches are very helpful for numerical analysis on compensator design for the unmanned aerial vehicles schemes. The simulation results confirm that the high performance stability test on unmanned aerial vehicle system has been met with the theoretical works.

In this paper, a quadcopter unmanned aerial vehicle (UAV) system based on neural-network enhanced dynamic inversion control is proposed for multiple real-world application scenarios. A sigma-pi neural network (SPNN) is used as the compensator to reduce the model error and improve the system performance in the presence of the uncertainties of UAV dynamics, payload, and environment. Besides, we present a technical framework for fast and robust implementation of multipurpose UAV systems and develop a testbed for the evaluation of UAV control system by using a high-precision optical motion capture system. Both simulation results and experiment results demonstrate that the SPNN can reduce the inversion errors related to UAV parameter uncertainties as well as tracking errors related to unknown disturbances and unmodeled dynamics. With the help of an online neural network (NN) learning mechanism, the entire system can achieve much higher accuracy in attitude and trajectory control than that achieved by conventional proportional-integral derivative based control systems under varying flight conditions.

Analysis of a hydrogen fuel cell-PV power system for small UAV

In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.

The purpose of this work is the analysis of the power supply system of unmanned aerial vehicle. One of the most important tasks of this system is to ensure the smallest possible losses in order to achieve greater endurance of the aircraft, which means – the ability to complete the mission. The presence of an inactive component of consumed power obviously reduces the efficiency of the entire unmanned aerial vehicle. Considering also that due to the influence of wind, speed changes and other factors, the power system spends a significant amount of time in the transition mode, the impact of this phenomenon becomes even more significant. Thus, the phenomenon of the occurrence of exchange power in the power supply system of an unmanned aerial vehicle is analyzed. One of the most common solutions for unmanned aerial vehicles is the use of a BLDC motor, which is a further development of DC motors and was created with the aim of improving their basic characteristics. This type of engine has gained its popularity due to numerous advantages: high reliability, efficiency, speed and others. The principles of control of the BLDС engine are given. The unmanned aerial vehicle power supply system, built on the basis of a buck converter and a bridge inverter, was analyzed. An equivalent circuit of the converter is built, taking into account the losses in the electric circuit. On its basis the relations for determining the value of exchange power in the power supply system are derived. With the help of these expressions, it is possible to determine the value of the exchange energy at an arbitrary time interval in the real power supply system of the aircraft. A model of the power supply system of an unmanned aerial vehicle with a BLDC engine was built in the Matlab Simulink software environment. A time diagram is obtained, on the basis of which it is possible to draw a conclusion about the content of exchange power in the converter circuit. To minimize this phenomenon, it is necessary to develop a compensation system or an intelligent control system.

The landing is one of the most dangerous maneuvers in the entirety of the flight phase of an Unmanned Aerial Vehicle (UAVs). Sudden changes in the environment cause issues regarding the stability of the drone, which poses a difficult challenge in landing the UAV precisely. To better the safety of any UAVs flying in urban areas, UAVs should be landed carefully, in a GPS-denied or network-disconnected environment, by using vision and inertial data. This paper presents UAV safe landing system which comprises of three sub-systems for detection of designated landing sites and autonomous pose correction, landing site inspection and landing flight control. This paper deals with vision-based target detection and pose correction system in-depth. The airborne vision system is utilized to recognize certain markers on the landing site. The information from the onboard visual sensors and Inertial Measurement Unit (IMU) is utilized to control and land UAV in a perfect landing trajectory, on a precise location. A series of experiments have been outlined to test and optimize the proposed method using Parrot AR.Drone 2.0.

Unmanned aerial vehicles (UAVs), including drones, are expected to be used in various fields. When used for inspection and in indoor spaces, a UAV is required to approach objects and obstacles. However, the risk of collision increases when the UAV approaches objects. In this study, we propose a sensor system for UAVs that can achieve omni-directional detection using a small number of multizone ranging time-of-flight (ToF) sensors. The sensor system consists of 24 ToF sensors on the propeller guard and two ToF sensors on the UAV and has a maximum detection distance of approximately 3500 mm. The sensor system can measure the omni-directional position of objects relative to a UAV in flight. Therefore, we think that the proposed system will be useful for UAV operations.

As interest grows in unmanned aerial vehicle (UAV) systems, UAVs have been proposed to take on increasingly more tasks that were previously assigned to humans. One such task is the delivery of goods within urban cities using UAVs, which would otherwise be delivered by terrestrial means. However, the limited endurance of UAVs due to limited onboard energy storage makes it challenging to practically employ UAV technology for deliveries across long routes. Furthermore, the relatively high cost of building UAV charging stations prevents the dense deployment of charging facilities. Solar-powered UAVs can ease this problem, as they do not require charging stations and can harvest solar power in the daytime. This paper introduces a solar-powered UAV goods delivery system to plan delivery missions with solar-powered UAVs (SPUs). In this study, when the SPUs run out of power, they charge themselves on landing places provided by customers instead of charging stations. Some advanced path planning algorithms are proposed to minimize the overall mission time in the statically charging efficiency environment. We further consider routing in the dynamically charging efficiency environment and propose some mission arrangement protocols to manage different missions in the system. The simulation results demonstrate that the algorithms proposed in our work perform significantly better than existing UAV path planning algorithms in solar-powered UAV systems.

This research is dedicated to developing an automatic landing system for shipborne unmanned aerial vehicles (UAVs) based on wireless precise positioning technology. The application scenario is practical for specific challenging and complex environmental conditions, such as the Global Positioning System (GPS) being disabled during wartime. The primary objective is to establish a precise and real-time dynamic wireless positioning system, ensuring that the UAV can autonomously land on the shipborne platform without relying on GPS. This work addresses several key aspects, including the implementation of an ultra-wideband (UWB) circuit module with a specific antenna design and RF front-end chip to enhance wireless signal reception. These modules play a crucial role in achieving accurate positioning, mitigating the limitations caused by GPS inaccuracy, thereby enhancing the overall performance and reception range of the system. Additionally, the study develops a wireless positioning algorithm to validate the effectiveness of automatic landing on the shipborne platform. The platform’s wave vibration is considered to provide a realistic landing system for shipborne UAVs. The UWB modules are practically installed on the shipborne platform, and the UAV and the autonomous three-body vessel are tested simultaneously in the outdoor open water space to verify the functionality, precision, and adaptability of the proposed UAV landing system. Results demonstrate that the UAV can autonomously fly from 200 m, approach, and automatically land on the moving shipborne platform without relying on GPS.

The article discusses about the system of information support for unmanned ground vehicle and un-manned aerial vehicles with an increased degree of autonomy, arising application difficulties, is considered. Some methodological approaches to calculating the requirements of electronic protection for the data transmission systems of unmanned ground vehicle and unmanned aerial vehicles with an increased degree of autonomy are given.

Optical flow (OF) plays a decisive role in visual situation awareness, detection and obstacle avoidance systems for unmanned aerial vehicles (UAVs), which are cyberphysical systems (CPSs) that interact with the environment through sensors and actuators. The use of cameras allows the integration of computer vision (CV) algorithms with the inertial navigation systems (INS). The movement of characteristics of the image fused with the dynamic of the UAVs allows us to improve the process of remoting sense, avoid obstacles or estimate the position and velocity of the UAV. In the literature, there are various algorithms to locate characteristics points between two consecutive images. However, the computation time and consumption of physical resources such as memory features are due to embedded systems. This chapter shows (i) how to integrate the movement of the pixel textures (OF) in the image with INS data, (ii) compares different algorithms to match points between consecutive images, (iii) implements a process to encounter points between consecutive images and (iv) implements a computationally less expensive and with less memory consumption algorithm. A case study about using the field-programmable gate array (FPGA) as part of the visual servoing is discussed showing how to integrate results into the CV hardware system of a UAV and addressing the need to handle issues such as multi-resolution.

The use of unmanned aerial vehicles (UAVs) in various military and civil applications is the subject of much current attention. With recent developments in personal computer technology, and the availability at affordable cost of peripherals, and electronic and optical sensors, UAVs for long endurance missions, with flight autonomy beyond the visual range, have become an attractive challenge for study in universities and research institutes. This paper describes the development of a target-lock-on optical remote sensing system to be used as a payload in a university-class UAV. To accomplish autonomous way-point navigation for the conduct of optical sensing surveillance, a gimbaled-platform with servo control and an Attitude and Heading Reference System (AHRS) navigation system for UAV position and attitude measurements have been developed. The UAV also utilises a Global Position System (GPS) receiver, a pressure altimeter, gyroscopes and an electric compass. A novel mathematical model is proposed to calculate the optimal parameters for orientating the CCD camera line of sight with a ground target, designated in real time from a ground control station. Both ground and flight test results have demonstrated the feasibility of the navigation control scheme and the UAV’s ability to conduct ground target acquisition and image transmission.

Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system