scholarly journals Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 391
Author(s):  
Luca Bigazzi ◽  
Stefano Gherardini ◽  
Giacomo Innocenti ◽  
Michele Basso
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Degrees Of Freedom ◽  
Vision System ◽  
Video Stream ◽  
Measurement Unit ◽  
Absolute Position ◽  
Light Load ◽  
Aerial Vehicles ◽  
Angular Velocities ◽  
Custom Hardware

In this paper, solutions for precise maneuvering of an autonomous small (e.g., 350-class) Unmanned Aerial Vehicles (UAVs) are designed and implemented from smart modifications of non expensive mass market technologies. The considered class of vehicles suffers from light load, and, therefore, only a limited amount of sensors and computing devices can be installed on-board. Then, to make the prototype capable of moving autonomously along a fixed trajectory, a “cyber-pilot”, able on demand to replace the human operator, has been implemented on an embedded control board. This cyber-pilot overrides the commands thanks to a custom hardware signal mixer. The drone is able to localize itself in the environment without ground assistance by using a camera possibly mounted on a 3 Degrees Of Freedom (DOF) gimbal suspension. A computer vision system elaborates the video stream pointing out land markers with known absolute position and orientation. This information is fused with accelerations from a 6-DOF Inertial Measurement Unit (IMU) to generate a “virtual sensor” which provides refined estimates of the pose, the absolute position, the speed and the angular velocities of the drone. Due to the importance of this sensor, several fusion strategies have been investigated. The resulting data are, finally, fed to a control algorithm featuring a number of uncoupled digital PID controllers which work to bring to zero the displacement from the desired trajectory.

scholarly journals An Embedded Platform for Positioning and Obstacle Detection for Small Unmanned Aerial Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1175
Author(s):  
Salvatore Ponte ◽  
Gennaro Ariante ◽  
Umberto Papa ◽  
Giuseppe Del Core
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Degrees Of Freedom ◽  
Estimation Algorithm ◽  
General Purpose ◽  
Obstacle Detection ◽  
Position Estimation ◽  
Unmanned Aircraft ◽  
Measurement Unit ◽  
Accelerometer Data ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAV) with on-board augmentation systems (UAS, Unmanned Aircraft System) have penetrated into civil and general-purpose applications, due to advances in battery technology, control components, avionics and rapidly falling prices. This paper describes the conceptual design and the validation campaigns performed for an embedded precision Positioning, field mapping, Obstacle Detection and Avoiding (PODA) platform, which uses commercial-off-the-shelf sensors, i.e., a 10-Degrees-of-Freedom Inertial Measurement Unit (10-DoF IMU) and a Light Detection and Ranging (LiDAR), managed by an Arduino Mega 2560 microcontroller with Wi-Fi capabilities. The PODA system, designed and tested for a commercial small quadcopter (Parrot Drones SAS Ar.Drone 2.0, Paris, France), estimates position, attitude and distance of the rotorcraft from an obstacle or a landing area, sending data to a PC-based ground station. The main design issues are presented, such as the necessary corrections of the IMU data (i.e., biases and measurement noise), and Kalman filtering techniques for attitude estimation, data fusion and position estimation from accelerometer data. The real-time multiple-sensor optimal state estimation algorithm, developed for the PODA platform and implemented on the Arduino, has been tested in typical aerospace application scenarios, such as General Visual Inspection (GVI), automatic landing and obstacle detection. Experimental results and simulations of various missions show the effectiveness of the approach.

Radiometric Quality Assessment of Video Sequences Acquired from UAV Photogrammetric Systems

2017 ◽  
Author(s):  
Damian Wierzbicki ◽  
Anna Fryskowska
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Video Stream ◽  
Video Data ◽  
Video Sequences ◽  
Wide Field ◽  
Sensing Applications ◽  
Aerial Vehicles ◽  
High Resolution Imagery ◽  
Low Altitude ◽  
Imagery Data

The issue of imagery data collection and its implementation in photogrammetric studies with the use of unmanned aerial vehicles is still valid and provides a wide field of research in the creation of new and expansion of existing solutions. It is particularly important to increase the accuracy of photogrammetric products. These days low altitude unmanned aerial vehicles are being used more and more often in photogrammetric applications. Compact digital cameras had acquired single, high-resolution imagery. Data obtained from low altitudes were often (and still are) used in mapping and 3D modelling. Due to the low costs of flights of UAV systems in comparison with traditional flights, applications of such platforms are also attractive for many remote sensing applications. However, due to the use of non-metric video cameras, one of the main problems when trying to automate the video data processing, is the video sequences’ relatively poor radiometric quality. The article addresses the issue of assessing the quality of the video imagery acquired from a low altitude UAV platform. The Authors presented quality Indicators dedicated to UAV video sequences. The method is based on the analysis of the video stream, obtained in the different weather and lighting conditions. As a result of the research, an objective quality index for video acquired from low altitudes was determined.

scholarly journals LOW-COST NAVIGATION AND GUIDANCE SYSTEMS FOR UNMANNED AERIAL VEHICLES — PART 2: ATTITUDE DETERMINATION AND CONTROL

2013 ◽  
Vol 20 (1) ◽  
pp. 97-126 ◽  
Author(s):  
Roberto Sabatini ◽  
Leopoldo Rodríguez ◽  
Anish Kaharkar ◽  
Celia Bartel ◽  
Tesheen Shaid ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Attitude Determination ◽  
Low Cost ◽  
Sensor Data ◽  
Measurement Unit ◽  
Medium Size ◽  
Integrated Navigation ◽  
Aerial Vehicles ◽  
Guidance Systems ◽  
And Control

ABSTRACT This paper presents the second part of the research activity performed by Cranfield University to assess the potential of low-cost navigation sensors for Unmanned Aerial Vehicles (UAVs). This part focuses on carrier-phase Global Navigation Satellite Systems (GNSS) for attitude determination and control of small to medium size UAVs. Recursive optimal estimation algorithms were developed for combining multiple attitude measurements obtained from different observation points (i.e., antenna locations), and their efficiencies were tested in various dynamic conditions. The proposed algorithms converged rapidly and produced the required output even during high dynamics manoeuvres. Results of theoretical performance analysis and simulation activities are presented in this paper, with emphasis on the advantages of the GNSS interferometric approach in UAV applications (i.e., low cost, high data-rate, low volume/weight, low signal processing requirements, etc.). The simulation activities focussed on the AEROSONDE UAV platform and considered the possible augmentation provided by interferometric GNSS techniques to a low-cost and low-weight/volume integrated navigation system (presented in the first part of this series) which employed a Vision-Based Navigation (VBN) system, a Micro-Electro-Mechanical Sensor (MEMS) based Inertial Measurement Unit (IMU) and code-range GNSS (i.e., GPS and GALILEO) for position and velocity computations. The integrated VBN-IMU-GNSS (VIG) system was augmented using the inteferometric GNSS Attitude Determination (GAD) sensor data and a comparison of the performance achieved with the VIG and VIG/GAD integrated Navigation and Guidance Systems (NGS) is presented in this paper. Finally, the data provided by these NGS are used to optimise the design of a hybrid controller employing Fuzzy Logic and Proportional-Integral-Derivative (PID) techniques for the AEROSONDE UAV.

Guidance Algorithm for Unmanned Aerial Vehicles on a Basis System of Technical Viewing

2019 ◽  
pp. 202-220
Author(s):  
Oleksii Pikenin ◽  
Oleksander Marynoshenko
Keyword(s):  
Control System ◽  
Environmental Monitoring ◽  
Unmanned Aerial Vehicles ◽  
Image Recognition ◽  
Aerial Photography ◽  
Vision System ◽  
Control Algorithms ◽  
Aerial Vehicles ◽  
Basis System ◽  
Coordinated Flight

The chapter considers a description of developed control system for a group of unmanned aerial vehicles (UAV) that has a software capable to continue the flight in case of failures by using alternative control algorithms. Control system is developed on vision system by using methods of image recognition. Grouped coordinated flight of UAVs can significantly improve the performance of surveillance processes, such as reconnaissance, image recognition, aerial photography, industrial and environmental monitoring, etc. But to control a group of UAVs is a quite difficult task. In this chapter, the authors propose a model that corresponds to the principle of construction by the leading UAVs. In the case of using this model, the parameters of the system motion are determined by the direction of motion, the speed, and the acceleration of the UAVs' driving. The control system based on the methods of image recognition expands the possibilities of coordinating the group of UAVs.

Appliсation of the phase correlation algorithm and its extension in the optical stabilizer of unmanned aerial vehicles

2017 ◽  
pp. 111-119
Author(s):  
O.L. Dreval ◽  
◽  
А.Yu. Doroshenko ◽  
◽  
Keyword(s):  
Computational Complexity ◽  
Embedded Systems ◽  
Unmanned Aerial Vehicles ◽  
Phase Correlation ◽  
Absolute Position ◽  
Correlation Algorithm ◽  
Aerial Vehicles ◽  
Algorithm Implementation

The use of the phase correlation algorithm as the basis of unmanned aerial vehicles (UAV) Optical Stabilizer on purpose to determine the displacement of the housing relative to the surface is described. Also, the possibility of application of the phase correlation algorithm extensions to increase accuracy and reduce computing costs is considered. The algorithm implementation in bot MATLAB language and C is provided. The computational complexity of the algorithm implementation in C was experimentally analyzed in the scope of embedded systems. The proposed algorithm gives not only an auxiliary possibility to stabilize the aircraft position but also to calculate absolute position of the aircraft in space.

scholarly journals Low-Cost Navigation and Guidance Systems for Unmanned Aerial Vehicles — Part 1: Vision-Based and Integrated Sensors

2012 ◽  
Vol 19 (2) ◽  
pp. 71-98 ◽  
Author(s):  
Roberto Sabatini ◽  
Celia Bartel ◽  
Anish Kaharkar ◽  
Tesheen Shaid ◽  
Leopoldo Rodriguez ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
System Architecture ◽  
Navigation System ◽  
Low Cost ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Integrated Navigation System ◽  
Aerial Vehicles ◽  
Vision Based Navigation ◽  
Mems Imu

Abstract In this paper we present a new low-cost navigation system designed for small size Unmanned Aerial Vehicles (UAVs) based on Vision-Based Navigation (VBN) and other avionics sensors. The main objective of our research was to design a compact, light and relatively inexpensive system capable of providing the Required Navigation Performance (RNP) in all phases of flight of a small UAV, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN were compared and the Appearance-Based Approach (ABA) was selected for implementation. Feature extraction and optical flow techniques were employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway and body rates. Additionally, we addressed the possible synergies between VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, as well as the aiding from Aircraft Dynamics Models (ADMs). In particular, by employing these sensors/models, we aimed to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) was developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UAV platform in real-time. Two different integrated navigation system architectures were implemented. The first used VBN at 20 Hz and GPS at 1 Hz to augment the MEMS-IMU running at 100 Hz. The second mode also included the ADM (computations performed at 100 Hz) to provide augmentation of the attitude channel. Simulation of these two modes was accomplished in a significant portion of the AEROSONDE UAV operational flight envelope and performing a variety of representative manoeuvres (i.e., straight climb, level turning, turning descent and climb, straight descent, etc.). Simulation of the first integrated navigation system architecture (VBN/IMU/GPS) showed that the integrated system can reach position, velocity and attitude accuracies compatible with CAT-II precision approach requirements. Simulation of the second system architecture (VBN/IMU/GPS/ADM) also showed promising results since the achieved attitude accuracy was higher using the ADM/VBS/IMU than using VBS/IMU only. However, due to rapid divergence of the ADM virtual sensor, there was a need for frequent re-initialisation of the ADM data module, which was strongly dependent on the UAV flight dynamics and the specific manoeuvring transitions performed

Using a Large 2 Degree of Freedom Tail for Autonomous Aerobatics on a Flapping Wing Unmanned Aerial Vehicle

2016 ◽  
Author(s):  
Luke Roberts ◽  
Hugh Bruck ◽  
S. K. Gupta
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Flapping Wing ◽  
Degree Of Freedom ◽  
Simple Method ◽  
Orientation Change ◽  
Aerial Vehicles ◽  
Angular Velocities ◽  
Aerial Vehicle ◽  
Rotary Wing

Flapping wing unmanned aerial vehicles (FWUAVs) provide an alternative to traditional platforms because they are more maneuverable than fixed wing platforms while being faster, quieter, and more natural looking than rotary wing platforms. While real birds are able to execute complex and highly controlled aerobatic maneuvers, executing FWUAV aerobatics presents unique challenges due to difficulty in execution of controlled quick orientation change. This paper demonstrates a simple method for using a large 2 degree of freedom tail for quick orientation changes and flight control, enabling execution of a pre-programmed backflip maneuver on the Robo Raven V, a hybrid FWUAV. The platform reached angular velocities of up to 420° per second during the maneuver.

scholarly journals Estimation of Airspeed, Angle of Attack, and Sideslip for Small Unmanned Aerial Vehicles (UAVs) Using a Micro-Pitot Tube

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2325
Author(s):  
Gennaro Ariante ◽  
Salvatore Ponte ◽  
Umberto Papa ◽  
Giuseppe Del Core
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Pressure Sensor ◽  
Dynamic Pressure ◽  
Estimation Error ◽  
Angle Of Attack ◽  
Noise Removal ◽  
Unmanned Aircraft ◽  
Pitot Tube ◽  
Measurement Unit ◽  
Aerial Vehicles

Fixed and rotary-wing unmanned aircraft systems (UASs), originally developed for military purposes, have widely spread in scientific, civilian, commercial, and recreational applications. Among the most interesting and challenging aspects of small UAS technology are endurance enhancement and autonomous flight; i.e., mission management and control. This paper proposes a practical method for estimation of true and calibrated airspeed, Angle of Attack (AOA), and Angle of Sideslip (AOS) for small unmanned aerial vehicles (UAVs, up to 20 kg mass, 1200 ft altitude above ground level, and airspeed of up to 100 knots) or light aircraft, for which weight, size, cost, and power-consumption requirements do not allow solutions used in large airplanes (typically, arrays of multi-hole Pitot probes). The sensors used in this research were a static and dynamic pressure sensor (“micro-Pitot tube” MPX2010DP differential pressure sensor) and a 10 degrees of freedom (DoF) inertial measurement unit (IMU) for attitude determination. Kalman and complementary filtering were applied for measurement noise removal and data fusion, respectively, achieving global exponential stability of the estimation error. The methodology was tested using experimental data from a prototype of the devised sensor suite, in various indoor-acquisition campaigns and laboratory tests under controlled conditions. AOA and AOS estimates were validated via correlation between the AOA measured by the micro-Pitot and vertical accelerometer measurements, since lift force can be modeled as a linear function of AOA in normal flight. The results confirmed the validity of the proposed approach, which could have interesting applications in energy-harvesting techniques.

scholarly journals Feature-Based Stereo Vision Relative Positioning Strategy for Formation Control of Unmanned Aerial Vehicles

2020 ◽  
Vol 9 (2) ◽  
pp. 1613-1617
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Formation Control ◽  
Vision System ◽  
Ground Truth ◽  
Flight Test ◽  
Relative Positioning ◽  
Ground Truth Data ◽  
Aerial Vehicles ◽  
Novel Approach ◽  
Positioning Strategy

As inspired by birds flying in flocks, their vision is one of the most critical components to enable them to respond to their neighbor’s motion. In this paper, a novel approach in developing a Vision System as the primary sensor for relative positioning in flight formation of a Leader-Follower scenario is introduced. To use the system in real-time and on-board of the unmanned aerial vehicles (UAVs) with up to 1.5 kilograms of payload capacity, few computing platforms are reviewed and evaluated. The study shows that the NVIDIA Jetson TX1 is the most suited platform for this project. In addition, several different techniques and approaches for developing the algorithm is discussed as well. As per system requirements and conducted study, the algorithm that is developed for this Vision System is based on Tracking and On-Line Machine Learning approach. Flight test has been performed to check the accuracy and reliability of the system, and the results indicate the minimum accuracy of 83% of the vision system against ground truth data.

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