Low‐cost flight test system for light aircrafts

This paper presents a cooperative perception system for multiple heterogeneous unmanned aerial vehicles (UAVs). It considers different kind of sensors: infrared and visual cameras and fire detectors. The system is based on a set of multipurpose low‐level image‐processing functions including segmentation, stabilization of sequences of images, and geo‐referencing, and it also involves data fusion algorithms for cooperative perception. It has been tested in field experiments that pursued autonomous multi‐UAV cooperative detection, monitoring, and measurement of forest fires. This paper presents the overall architecture of the perception system, describes some of the implemented cooperative perception techniques, and shows experimental results on automatic forest fire detection and localization with cooperating UAVs. © 2006 Wiley Periodicals, Inc.

Autonomous formation flight has been a fast growing research area during the last decade. Unmanned aerial vehicles (UAVs) can achieve a wide range of benefits from formation flight, such as fuel saving and aerial refueling. In this paper, the development of a small UAV system, called Swallow UAV system, as testbed of formation flight, is presented. Swallow UAV system includes Swallow UAV itself and a Hardware-In-the-Loop Simulation Environment (HILSE). Swallow UAV is a fixed wing, pusher-type, electric motor driven aircraft with 1.4 meter wing span. Its avionics system includes an onboard computer and a sensor system with GPS, IMU, air data sensor, and motor monitoring unit to provide position, velocity, inertial data, and system health. Communications between UAVs and the ground control station (GCS) are conducted by wireless modem. A fuzzy navigation and control system (FNCS) is developed for the waypoint navigation and attitude control of the aircraft. GPS velocity based pseudo attitude and fuzzy logic control are applied to FNCS for attitude estimation and fast implementation of controllers. HILSE is built to verify FNCS with onboard computer on the ground. X-Plane flight simulator is applied to simulate aircraft dynamics and output sensor data. GCS software, built with data distributor, transmits these data to the onboard computer and receive control signal from it so the onboard computer can control the aircraft in X-Plane. HILSE can perform pre-flight test for software debug, which improves safety and efficiency of flight test. The design of Swallow UAV is verified by flight test and the result shows that it has sufficient flight performance with waypoint navigation and attitude control capability to be used as a formation flight testbed.

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.

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.

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.

For their size and cost advantage, small or so called man-portable unmanned aerial vehicles (UAV) have gathered considerable interests over the past few years. Although these vehicles may be considered inexpensive and dispensable, the imposed mission requirements are very demanding and complex relying on fully autonomous and intelligent self decision making operations. Most of small UAV currently in service utilizes commercial-off-the-shelf (COTS) autopilot which often puts constraints on vehicle maneuverability and programming flexibility. The motivation for this study is to develop an integrated guidance, navigation and control (GNC) system that provides flexibility in various engineering aspects of small UAV, ranging from a new platform design to autopilot synthesis. This paper presents some results from the initial design and development effort; hardware integration of necessary sensors, a processor selection, and flight testing. Strap-down inertial navigation principle is considered. Flight test data are utilized for the post-flight numerical studies in order to understand the system limitations and characteristics, which is a crucial step for the software design.

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.

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

Inspecting overhead cables using autonomous Unmanned Aerial Vehicles (UAVs) is an affordable and promising future solution for providing a clear picture of the energy infrastructure. However, most of today's UAVs for powerline inspection are designed to use the Global Navigation Satellite System (GNSS) to follow the power pylons which compromises the detection accuracy. In this paper, we provide an overview of the current state-of-the-art in sensors that can be used to remotely detect power lines using UAVs. The work compares 20+ low-cost, low-power, and light-weight sensors. The performance of 6 different kinds of sensors is evaluated thoroughly in a real outdoor powerline test setup using a custom UAV. The sensor data is obtained and analyzed using Robot Operating System (ROS) and is provided openly.

A system of unmanned aerial vehicles (UAVs) can be used to provide uninterrupted service to customers by handing the mission from a weary UAV to a fresh one in an automated manner. Toward such a system, we discuss the development of two components: ROS GPS waypoint tracking package and centralized task allocation network system (CTANS). The ROS GPS waypoint tracking package is the first open source software developed for the AR Drone 2.0 UAV that enables integration with a larger system. CTANS connects the UAV host computers to a central computer, receives the UAV status information and distributes GPS flight directives. CTANS is responsible for distributing the commands to the UAVs to ensure mission handoffs are conducted as required. This multi-UAV control software testbed was used to implement a small scale, outdoor, persistent, GPS based patrol mission with two UAVs.

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.

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.

With the rapid development of intelligent information technology, human–computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human–computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human–computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human–computer interaction and has great development potential. The characteristics of human–computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human–computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious advantages.