Visual control of multi-rotor UAVs

Recent miniaturization of computer hardware, MEMs sensors, and high energy density batteries have enabled highly capable mobile robots to become available at low cost. This has driven the rapid expansion of interest in multi-rotor unmanned aerial vehicles. Another area which has expanded simultaneously is small powerful computers, in the form of smartphones, which nearly always have a camera attached, many of which now contain a OpenCL compatible graphics processing units. By combining the results of those two developments a low-cost multi-rotor UAV can be produced with a low-power onboard computer capable of real-time computer vision. The system should also use general purpose computer vision software to facilitate a variety of experiments. To demonstrate this I have built a quadrotor UAV based on control hardware from the Pixhawk project, and paired it with an ARM based single board computer, similar those in high-end smartphones. The quadrotor weights 980 g and has a flight time of 10 minutes. The onboard computer capable of running a pose estimation algorithm above the 10 Hz requirement for stable visual control of a quadrotor. A feature tracking algorithm was developed for efficient pose estimation, which relaxed the requirement for outlier rejection during matching. Compared with a RANSAC- only algorithm the pose estimates were less variable with a Z-axis standard deviation 0.2 cm compared with 2.4 cm for RANSAC. Processing time per frame was also faster with tracking, with 95 % confidence that tracking would process the frame within 50 ms, while for RANSAC the 95 % confidence time was 73 ms. The onboard computer ran the algorithm with a total system load of less than 25 %. All computer vision software uses the OpenCV library for common computer vision algorithms, fulfilling the requirement for running general purpose software. The tracking algorithm was used to demonstrate the capability of the system by per- forming visual servoing of the quadrotor (after manual takeoff). Response to external perturbations was poor however, requiring manual intervention to avoid crashing. This was due to poor visual controller tuning, and to variations in image acquisition and attitude estimate timing due to using free running image acquisition. The system, and the tracking algorithm, serve as proof of concept that visual control of a quadrotor is possible using small low-power computers and general purpose computer vision software

Vision-Based navigation system for unmanned aerial vehicles

Mención Internacional en el título de doctorThe main objective of this dissertation is to provide Unmanned Aerial Vehicles (UAVs) with a robust navigation system; in order to allow the UAVs to perform complex tasks autonomously and in real-time. The proposed algorithms deal with solving the navigation problem for outdoor as well as indoor environments, mainly based on visual information that is captured by monocular cameras. In addition, this dissertation presents the advantages of using the visual sensors as the main source of data, or complementing other sensors in providing useful information; in order to improve the accuracy and the robustness of the sensing purposes. The dissertation mainly covers several research topics based on computer vision techniques: (I) Pose Estimation, to provide a solution for estimating the 6D pose of the UAV. This algorithm is based on the combination of SIFT detector and FREAK descriptor; which maintains the performance of the feature points matching and decreases the computational time. Thereafter, the pose estimation problem is solved based on the decomposition of the world-to-frame and frame-to-frame homographies. (II) Obstacle Detection and Collision Avoidance, in which, the UAV is able to sense and detect the frontal obstacles that are situated in its path. The detection algorithm mimics the human behaviors for detecting the approaching obstacles; by analyzing the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. Then, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, the algorithm extracts the collision-free zones around the obstacle, and combining with the tracked waypoints, the UAV performs the avoidance maneuver. (III) Navigation Guidance, which generates the waypoints to determine the flight path based on environment and the situated obstacles. Then provide a strategy to follow the path segments and in an efficient way and perform the flight maneuver smoothly. (IV) Visual Servoing, to offer different control solutions (Fuzzy Logic Control (FLC) and PID), based on the obtained visual information; in order to achieve the flight stability as well as to perform the correct maneuver; to avoid the possible collisions and track the waypoints. All the proposed algorithms have been verified with real flights in both indoor and outdoor environments, taking into consideration the visual conditions; such as illumination and textures. The obtained results have been validated against other systems; such as VICON motion capture system, DGPS in the case of pose estimate algorithm. In addition, the proposed algorithms have been compared with several previous works in the state of the art, and are results proves the improvement in the accuracy and the robustness of the proposed algorithms. Finally, this dissertation concludes that the visual sensors have the advantages of lightweight and low consumption and provide reliable information, which is considered as a powerful tool in the navigation systems to increase the autonomy of the UAVs for real-world applications.El objetivo principal de esta tesis es proporcionar Vehiculos Aereos no Tripulados (UAVs) con un sistema de navegacion robusto, para permitir a los UAVs realizar tareas complejas de forma autonoma y en tiempo real. Los algoritmos propuestos tratan de resolver problemas de la navegacion tanto en ambientes interiores como al aire libre basandose principalmente en la informacion visual captada por las camaras monoculares. Ademas, esta tesis doctoral presenta la ventaja de usar sensores visuales bien como fuente principal de datos o complementando a otros sensores en el suministro de informacion util, con el fin de mejorar la precision y la robustez de los procesos de deteccion. La tesis cubre, principalmente, varios temas de investigacion basados en tecnicas de vision por computador: (I) Estimacion de la Posicion y la Orientacion (Pose), para proporcionar una solucion a la estimacion de la posicion y orientacion en 6D del UAV. Este algoritmo se basa en la combinacion del detector SIFT y el descriptor FREAK, que mantiene el desempeno del a funcion de puntos de coincidencia y disminuye el tiempo computacional. De esta manera, se soluciona el problema de la estimacion de la posicion basandose en la descomposicion de las homografias mundo a imagen e imagen a imagen. (II) Deteccion obstaculos y elusion colisiones, donde el UAV es capaz de percibir y detectar los obstaculos frontales que se encuentran en su camino. El algoritmo de deteccion imita comportamientos humanos para detectar los obstaculos que se acercan, mediante el analisis de la magnitud del cambio de los puntos caracteristicos detectados de referencia, combinado con los ratios de expansion de los contornos convexos construidos alrededor de los puntos caracteristicos detectados en frames consecutivos. A continuacion, comparando la proporcion del area del obstaculo y la posicion del UAV, el metodo decide si el obstaculo detectado puede provocar una colision. Por ultimo, el algoritmo extrae las zonas libres de colision alrededor del obstaculo y combinandolo con los puntos de referencia, elUAV realiza la maniobra de evasion. (III) Guiado de navegacion, que genera los puntos de referencia para determinar la trayectoria de vuelo basada en el entorno y en los obstaculos detectados que encuentra. Proporciona una estrategia para seguir los segmentos del trazado de una manera eficiente y realizar la maniobra de vuelo con suavidad. (IV) Guiado por Vision, para ofrecer soluciones de control diferentes (Control de Logica Fuzzy (FLC) y PID), basados en la informacion visual obtenida con el fin de lograr la estabilidad de vuelo, asi como realizar la maniobra correcta para evitar posibles colisiones y seguir los puntos de referencia. Todos los algoritmos propuestos han sido verificados con vuelos reales en ambientes exteriores e interiores, tomando en consideracion condiciones visuales como la iluminacion y las texturas. Los resultados obtenidos han sido validados con otros sistemas: como el sistema de captura de movimiento VICON y DGPS en el caso del algoritmo de estimacion de la posicion y orientacion. Ademas, los algoritmos propuestos han sido comparados con trabajos anteriores recogidos en el estado del arte con resultados que demuestran una mejora de la precision y la robustez de los algoritmos propuestos. Esta tesis doctoral concluye que los sensores visuales tienen las ventajes de tener un peso ligero y un bajo consumo y, proporcionar informacion fiable, lo cual lo hace una poderosa herramienta en los sistemas de navegacion para aumentar la autonomia de los UAVs en aplicaciones del mundo real.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Carlo Regazzoni.- Secretario: Fernando García Fernández.- Vocal: Pascual Campoy Cerver

Single chip solution for stabilization control & monocular visual servoing of small-scale quadrotor helicopter

This thesis documents the research undertaken to develop a high-performing design of a small-scale quadrotor (four-rotor) helicopter capable of delivering the speed and robustness required for agile motion while also featuring an autonomous visual servoing capability within the size, weight, and power (SWaP) constraint package. The state of the art research was reviewed, and the areas in the existing design methodologies that can potentially be improved were identified, which included development of a comprehensive dynamics model of quadrotor, design and construction of a performance optimized prototype vehicle, high-performance actuator design, design of a robust attitude stabilization controller, and a single chip solution for autonomous vision based position control. The gaps in the current art of designing each component were addressed individually. The outcomes of the corresponding development activities include a high-fidelity dynamics and control model of the vehicle. The model was developed using multi-body bond graph modeling approach to incorporate the dynamic interactions between the frame body and propulsion system. Using an algorithmic size, payload capacity, and flight endurance optimization approach, a quadrotor prototype was designed and constructed. In order to conform to the optimized geometric and performance parameters, the frame of the prototype was constructed using printed circuit board (PCB) technology and processing power was integrated using a single chip field programmable gate array (FPGA) technology. Furthermore, to actuate the quadrotor at a high update rate while also improving the power efficiency of the actuation system, a ground up FPGA based brushless direct current (BLDC) motor driver was designed using a low-loss commutation scheme and hall effect sensors. A proportional-integral-derivative (PID) technology based closed loop motor speed controller was also implemented in the same FPGA hardware for precise speed control of the motors. In addition, a novel control law was formulated for robust attitude stabilization by adopting a cascaded architecture of active disturbance rejection control (ADRC) technology and PID control technology. Using the same single FPGA chip to drive an on-board downward looking camera, a monocular visual servoing solution was developed to integrate an autonomous position control feature with the quadrotor. Accordingly, a numerically simple relative position estimation technique was implemented in FPGA hardware that relies on a passive landmark/target for 3-D position estimation. The functionality and effectiveness of the synthesized design were evaluated by performance benchmarking experiments conducted on each individual component as well as on the complete system constructed from these components. It was observed that the proposed small-scale quadrotor, even though just 43 cm in diameter, can lift 434 gm of payload while operating for 18 min. Among the ground up designed components, the FPGA based motor driver demonstrated a maximum of 4% improvement in the power consumption and at the same time can handle a command update at a rate of 16 kHz. The cascaded attitude stabilization controller can asymptotically stabilize the vehicle within 426 ms of the command update. Robust control performance under stochastic wind gusts is also observed from the stabilization controller. Finally, the single chip FPGA based monocular visual servoing solution can estimate pose information at the camera rate of 37 fps and accordingly the quadrotor can autonomously climb/descend and/or hover over a passive target

A survey of single and multi-UAV aerial manipulation

Aerial manipulation has direct application prospects in environment, construction, forestry, agriculture, search, and rescue. It can be used to pick and place objects and hence can be used for transportation of goods. Aerial manipulation can be used to perform operations in environments inaccessible or unsafe for human workers. This paper is a survey of recent research in aerial manipulation. The aerial manipulation research has diverse aspects, which include the designing of aerial manipulation platforms, manipulators, grippers, the control of aerial platform and manipulators, the interaction of aerial manipulator with the environment, through forces and torque. In particular, the review paper presents the survey of the airborne platforms that can be used for aerial manipulation including the new aerial platforms with aerial manipulation capability. We also classified the aerial grippers and aerial manipulators based on their designs and characteristics. The recent contributions regarding the control of the aerial manipulator platform is also discussed. The environment interaction of aerial manipulators is also surveyed which includes, different strategies used for end-effectors interaction with the environment, application of force, application of torque and visual servoing. A recent and growing interest of researchers about the multi-UAV collaborative aerial manipulation was also noticed and hence different strategies for collaborative aerial manipulation are also surveyed, discussed and critically analyzed. Some key challenges regarding outdoor aerial manipulation and energy constraints in aerial manipulation are also discussed

Vision-based automatic landing of a rotary UAV

A hybrid-like (continuous and discrete-event) approach to controlling a small multi-rotor unmanned aerial system (UAS) while landing on a moving platform is described. The landing scheme is based on positioning visual markers on a landing platform in a detectable pattern. After the onboard camera detects the object pattern, the inner control algorithm sends visual-based servo-commands to align the multi-rotor with the targets. This method is less computationally complex as it uses color-based object detection applied to a geometric pattern instead of feature tracking algorithms, and has the advantage of not requiring the distance to the objects to be calculated. The continuous approach accounts for the UAV and the platform rolling/pitching/yawing, which is essential for a real-time landing on a moving target such as a ship. A discrete-event supervisor working in parallel with the inner controller is designed to assist the automatic landing of a multi-rotor UAV on a moving target. This supervisory control strategy allows the pilot and crew to make time-critical decisions when exceptions, such as losing targets from the field of view, occur. The developed supervisor improves the low-level vision-based auto-landing system and high-level human-machine interface. The proposed hybrid-like approach was tested in simulation using a quadcopter model in Virtual Robotics Experimentation Platform (V-REP) working in parallel with Robot Operating System (ROS). Finally, this method was validated in a series of real-time experiments with indoor and outdoor quadcopters landing on both static and moving platforms. The developed prototype system has demonstrated the capability of landing within 25 cm of the desired point of touchdown. This auto-landing system is small (100 x 100 mm), light-weight (100 g), and consumes little power (under 2 W)

Development Of A Quadrotor Testbed For Control And Sensor Development

A quadrotor is an under actuated unmanned aerial vehicle (UAV) which uses thrust from four rotors to provide six degrees of freedom. This thesis outlines the development of a general purpose test bed that can be used for sensor and control algorithm development. The system includes the means to simulate a proposed controller and then a hardware in the loop implementation using the same software. The test bed was assembled and verified with a linear controller for both attitude and position control using feedback from an IMU (Inertial measurement Unit) and a Global Position System (GPS) sensor. The linear controller was first implemented as a PID controller which attempts to control the attitude of the quadrotor. The controller was simulated successfully and then experiments were conducted on a DraganFlyer X-Pro quadrotor to verify the closed loop control. The experiments conducted checked the response of the quadrotor angles to the commanded angles. The controller gains were tuned to provide stable hover in all three angles. The Videre stereo vision system was investigated as a sensor to estimate height of the UAV above the ground. Experiments were performed that show that show static (no motion of the camera) estimates over the range 0.5 - 4 meters. The accuracy of these measurements suggest that the system may provide improved height estimation, over WAAS corrected GPS. A means to add this sensor into the UAV test bed is discussed

Autonomous Aerial Manipulation Using a Quadrotor

This paper presents an implementation of autonomous indoor aerial gripping using a low-cost, custom-built quadrotor. Such research extends the typical functionality of micro air vehicles (MAV) from passive observation and sensing to dynamic interaction with the environment. To achieve this, three major challenges are overcome: precise positioning, sensing and manipulation of the object, and stabilization in the presence of disturbance due to interaction with the object. Navigation in both indoor and outdoor unstructured, Global Positioning System-denied (GPS-denied) environments is achieved using a visual Simultaneous Localization and Mapping (SLAM) algorithm that relies on an onboard monocular camera. A secondary camera, capable of detecting infrared light sources, is used to estimate the 3D location of the object, while an under-actuated and passively compliant manipulator is designed for effective gripping under uncertainty. The system utilizes nested ProportionalIntegral-Derivative (PID) controllers for attitude stabilization, vision-based navigation, and gripping. The quadrotor is therefore able to autonomously navigate, locate, and grasp an object, using only onboard sensors

Autonomous Visual Navigation of a Quadrotor VTOL in complex and dense environments

This thesis presents a system design of a micro aerial vehicle platform, specifically a quadrotor, that is aimed at autonomous vision-based reactive obstacle avoidance in dense and complex environments. Most modern aerial system are incapable of autonomously navigating in environments with a high density of trees and bushes. The presented quadrotor design uses leading-edge technologies and inexpensive off-the-shelf components to build a system that presents a step forward in technologies aimed at overcoming the issues with dense and complex environments. Several major system requirements were met to make the design effective and safe. It had to be completely autonomous in standard operations and have a manual override function. It had to have its computational capability completely on-board along with vision processing ability. As such, all state estimation and visual guidance had to be performed on-board the vehicle, removing the need for remote connection which can easily fail in forest-like environments. The quadrotor had to be made from mostly off-the-shelf components to reduce cost and make it replicable. It also had to remain under 2kg to meet Australian commercial aerial vehicle regulations regarding licencing. In order to meet the system requirements, many design decisions were developed and altered as needed. The main body of the quadrotor platform was based on off-the-shelf hobby assemblies. A Pixhawk 2.1 was the flight controller used due to its open-source code and design which included all sensors needed for state estimation, has manual override for control, and control the motors. A leading-edge computational device called the NVIDIA Tegra TX2 was used for vision processing on the quadrotor. The NVIDIA Tegra TX2's embedded NVIDIA Graphics Processing Unit (GPU), is compact and consumes low amounts of power. It also is capable of estimating dense optical flow on the GPU at rates of 120Hz when using a camera that outputs grey-scale images at a resolution of 376x240. The vision processor is responsible for providing directional guidance to the on-board flight controller. A design decision during the project was to include a 3-axis gimbal to stabilise the camera. The quadrotor was shown to be able to hover and locally move both indoors and outdoors using the optical flow measurements. Optical flow measurements give a sense of velocity which can be integrated to get a position estimate, though it was susceptible to drift. The drift was compensated using a combination of recognisable targets and positioning systems such as GPS. The experimental data obtained during the project showed that the algorithms presented in this thesis are capable of performing reactive obstacle avoidance. The reactive obstacle avoidance experiments were performed in both simulation and in real world environments, including the dense forest-like environments. By fusing vehicle speed estimates with optical flow measurements, visible points in 3D space can have their distance estimated relative to the quadrotor. By projecting a 3D cylinder in the direction of travel onto the camera plane, the system can perform reactive obstacle avoidance by steering the cylinder (direction of travel) to a point with minimal interference. This system is intended to augment a point to point navigation system such that the quadrotor responds to fine obstacle that may have otherwise not been detected