Visual guidance of unmanned aerial manipulators

The ability to fly has greatly expanded the possibilities for robots to perform surveillance, inspection or map generation tasks. Yet it was only in recent years that research in aerial robotics was mature enough to allow active interactions with the environment. The robots responsible for these interactions are called aerial manipulators and usually combine a multirotor platform and one or more robotic arms. The main objective of this thesis is to formalize the concept of aerial manipulator and present guidance methods, using visual information, to provide them with autonomous functionalities. A key competence to control an aerial manipulator is the ability to localize it in the environment. Traditionally, this localization has required external infrastructure of sensors (e.g., GPS or IR cameras), restricting the real applications. Furthermore, localization methods with on-board sensors, exported from other robotics fields such as simultaneous localization and mapping (SLAM), require large computational units becoming a handicap in vehicles where size, load, and power consumption are important restrictions. In this regard, this thesis proposes a method to estimate the state of the vehicle (i.e., position, orientation, velocity and acceleration) by means of on-board, low-cost, light-weight and high-rate sensors. With the physical complexity of these robots, it is required to use advanced control techniques during navigation. Thanks to their redundancy on degrees-of-freedom, they offer the possibility to accomplish not only with mobility requirements but with other tasks simultaneously and hierarchically, prioritizing them depending on their impact to the overall mission success. In this work we present such control laws and define a number of these tasks to drive the vehicle using visual information, guarantee the robot integrity during flight, and improve the platform stability or increase arm operability. The main contributions of this research work are threefold: (1) Present a localization technique to allow autonomous navigation, this method is specifically designed for aerial platforms with size, load and computational burden restrictions. (2) Obtain control commands to drive the vehicle using visual information (visual servo). (3) Integrate the visual servo commands into a hierarchical control law by exploiting the redundancy of the robot to accomplish secondary tasks during flight. These tasks are specific for aerial manipulators and they are also provided. All the techniques presented in this document have been validated throughout extensive experimentation with real robotic platforms.La capacitat de volar ha incrementat molt les possibilitats dels robots per a realitzar tasques de vigilància, inspecció o generació de mapes. Tot i això, no és fins fa pocs anys que la recerca en robòtica aèria ha estat prou madura com per començar a permetre interaccions amb l’entorn d’una manera activa. Els robots per a fer-ho s’anomenen manipuladors aeris i habitualment combinen una plataforma multirotor i un braç robòtic. L’objectiu d’aquesta tesi és formalitzar el concepte de manipulador aeri i presentar mètodes de guiatge, utilitzant informació visual, per dotar d’autonomia aquest tipus de vehicles. Una competència clau per controlar un manipulador aeri és la capacitat de localitzar-se en l’entorn. Tradicionalment aquesta localització ha requerit d’infraestructura sensorial externa (GPS, càmeres IR, etc.), limitant així les aplicacions reals. Pel contrari, sistemes de localització exportats d’altres camps de la robòtica basats en sensors a bord, com per exemple mètodes de localització i mapejat simultànis (SLAM), requereixen de gran capacitat de còmput, característica que penalitza molt en vehicles on la mida, pes i consum elèctric son grans restriccions. En aquest sentit, aquesta tesi proposa un mètode d’estimació d’estat del robot (posició, velocitat, orientació i acceleració) a partir de sensors instal·lats a bord, de baix cost, baix consum computacional i que proporcionen mesures a alta freqüència. Degut a la complexitat física d’aquests robots, és necessari l’ús de tècniques de control avançades. Gràcies a la seva redundància de graus de llibertat, aquests robots ens ofereixen la possibilitat de complir amb els requeriments de mobilitat i, simultàniament, realitzar tasques de manera jeràrquica, ordenant-les segons l’impacte en l’acompliment de la missió. En aquest treball es presenten aquestes lleis de control, juntament amb la descripció de tasques per tal de guiar visualment el vehicle, garantir la integritat del robot durant el vol, millorar de l’estabilitat del vehicle o augmentar la manipulabilitat del braç. Aquesta tesi es centra en tres aspectes fonamentals: (1) Presentar una tècnica de localització per dotar d’autonomia el robot. Aquest mètode està especialment dissenyat per a plataformes amb restriccions de capacitat computacional, mida i pes. (2) Obtenir les comandes de control necessàries per guiar el vehicle a partir d’informació visual. (3) Integrar aquestes accions dins una estructura de control jeràrquica utilitzant la redundància del robot per complir altres tasques durant el vol. Aquestes tasques son específiques per a manipuladors aeris i també es defineixen en aquest document. Totes les tècniques presentades en aquesta tesi han estat avaluades de manera experimental amb plataformes robòtiques real

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms

A distributed optimization framework for localization and formation control: applications to vision-based measurements

Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures

3-Dimensional Building Details from Aerial Photography for Internet Maps

This paper introduces the automated characterization of real estate (real property) for Internet mapping. It proposes a processing framework to achieve this task from vertical aerial photography and associated property information. A demonstration of the feasibility of an automated solution builds on test data from the Austrian City of Graz. Information is extracted from vertical aerial photography and various data products derived from that photography in the form of a true orthophoto, a dense digital surface model and digital terrain model, and a classification of land cover. Maps of cadastral property boundaries aid in defining real properties. Our goal is to develop a table for each property with descriptive numbers about the buildings, their dimensions, number of floors, number of windows, roof shapes, impervious surfaces, garages, sheds, vegetation, presence of a basement floor, and other descriptors of interest for each and every property of a city. From aerial sources, at a pixel size of 10 cm, we show that we have obtained positional accuracies in the range of a single pixel, an accuracy of areas in the 10% range, floor counts at an accuracy of 93% and window counts at 86% accuracy. We also introduce 3D point clouds of facades and their creation from vertical aerial photography, and how these point clouds can support the definition of complex facades

분산된 로터로 구동되는 비행 스켈레톤 시스템의 디자인 상태추정 및 제어

학위논문(박사)--서울대학교 대학원 :공과대학 기계항공공학부,2020. 2. 이동준.In this thesis, we present key theoretical components for realizing flying aerial skeleton system called LASDRA (large-size aerial skeleton with distributed rotor actuation). Aerial skeletons are articulated aerial robots actuated by distributed rotors including both ground connected type and flying type. These systems have recently attracted interest and are being actively researched in several research groups, with the expectation of applying those for aerial manipulation in distant/narrow places, or for the performance with entertaining purpose such as drone shows. Among the aerial skeleton systems, LASDRA system, proposed by our group has some significant advantages over the other skeleton systems that it is capable of free SE(3) motion by omni-directional wrench generation of each link, and also the system can be operated with wide range of configuration because of the 3DOF (degrees of freedom) inter-link rotation enabled by cable connection among the link modules. To realize this LASDRA system, following three components are crucial: 1) a link module that can produce omni-directional force and torque and enough feasible wrench space; 2) pose and posture estimation algorithm for an articulated system with high degrees of freedom; and 3) a motion generation framework that can provide seemingly natural motion while being able to generate desired motion (e.g., linear and angular velocity) for the entire body. The main contributions of this thesis is theoretically developing these three components, and verifying these through outdoor flight experiment with a real LASDRA system. First of all, a link module for the LASDRA system is designed with proposed constrained optimization problem, maximizing the guaranteed feasible force and torque for any direction while also incorporating some constraints (e.g., avoiding inter-rotor air-flow interference) to directly obtain feasible solution. Also, an issue of ESC-induced (electronic speed control) singularity is first introduced in the literature which is inevitably caused by bi-directional thrust generation with sensorless actuators, and handled with a novel control allocation called selective mapping. Then for the state estimation of the entire LASDRA system, constrained Kalman filter based estimation algorithm is proposed that can provide estimation result satisfying kinematic constraint of the system, also along with a semi-distributed version of the algorithm to endow with system scalability. Lastly, CPG-based motion generation framework is presented that can generate natural biomimetic motion, and by exploiting the inverse CPG model obtained with machine learning method, it becomes possible to generate certain desired motion while still making CPG generated natural motion.본 논문에서는 비행 스켈레톤 시스템 LASDRA (large-size aerial skeleton with distributed rotor actuation) 의 구현을 위해 요구되는 핵심 기법들을 제안하며, 이를 실제 LASDRA 시스템의 실외 비행을 통해 검증한다. 제안된 기법은 1) 전방향으로 힘과 토크를 낼 수 있고 충분한 가용 렌치공간을 가진 링크 모듈, 2) 높은 자유도의 다관절구조 시스템을 위한 위치 및 자세 추정 알고리즘, 3) 자연스러운 움직임을 내는 동시에 전체 시스템이 속도, 각속도 등 원하는 움직임을 내도록 할 수 있는 모션 생성 프레임워크로 구성된다. 본 논문에서는 우선 링크 모듈의 디자인을 위해 전방향으로 보장되는 힘과 토크의 크기를 최대화하는 구속 최적화를 사용하고, 실제 적용가능한 해를 얻기 위해 몇가지 구속조건(로터 간 공기 흐름 간섭의 회피 등)을 고려한다. 또한 센서가 없는 액츄에이터로 양방향 추력을 내는 것에서 야기되는 ESC 유발 특이점 (ESC-induced singularity) 이라는 문제를 처음으로 소개하고, 이를 해결하기 위해 선택적 맵핑 (selective mapping) 이라는 기법을 제시한다. 전체 LASDRA 시스템의 상태추정을 위해 시스템의 기구학적 구속조건을 만족하는 결과를 얻을 수 있도록 구속 칼만 필터 기반의 상태추정 기법을 제시하고, 시스템 확장성을 고려하여 반 분산 (semi-distributed) 개념의 알고리즘을 함께 제시한다. 마지막으로 본 논문에서는 자연스러운 움직임의 생성을 위하여 CPG 기반의 모션 생성 프레임워크를 제안하며, 기계 학습 방법을 통해 CPG 역연산 모델을 얻음으로써 전체 시스템이 원하는 움직임을 낼 수 있도록 한다.1 Introduction 1 1.1 Motivation and Background 1 1.2 Research Problems and Approach 3 1.3 Preview of Contributions 5 2 Omni-Directional Aerial Robot 7 2.1 Introduction 7 2.2 Mechanical Design 12 2.2.1 Design Description 12 2.2.2 Wrench-Maximizing Design Optimization 13 2.3 System Modeling and Control Design 20 2.3.1 System Modeling 20 2.3.2 Pose Trajectory Tracking Control 22 2.3.3 Hybrid Pose/Wrench Control 22 2.3.4 PSPM-Based Teleoperation 24 2.4 Control Allocation with Selective Mapping 27 2.4.1 Infinity-Norm Minimization 27 2.4.2 ESC-Induced Singularity and Selective Mapping 29 2.5 Experiment 38 2.5.1 System Setup 38 2.5.2 Experiment Results 41 2.6 Conclusion 49 3 Pose and Posture Estimation of an Aerial Skeleton System 51 3.1 Introduction 51 3.2 Preliminary 53 3.3 Pose and Posture Estimation 55 3.3.1 Estimation Algorithm via SCKF 55 3.3.2 Semi-Distributed Version of Algorithm 59 3.4 Simulation 62 3.5 Experiment 65 3.5.1 System Setup 65 3.5.2 Experiment of SCKF-Based Estimation Algorithm 66 3.6 Conclusion 69 4 CPG-Based Motion Generation 71 4.1 Introduction 71 4.2 Description of Entire Framework 75 4.2.1 LASDRA System 75 4.2.2 Snake-Like Robot & Pivotboard 77 4.3 CPG Model 79 4.3.1 LASDRA System 79 4.3.2 Snake-Like Robot 80 4.3.3 Pivotboard 83 4.4 Target Pose Calculation with Expected Physics 84 4.5 Inverse Model Learning 86 4.5.1 LASDRA System 86 4.5.2 Snake-Like Robot 89 4.5.3 Pivotboard 90 4.6 CPG Parameter Adaptation 93 4.7 Simulation 94 4.7.1 LASDRA System 94 4.7.2 Snake-Like Robot & Pivotboard 97 4.8 Conclusion 101 5 Outdoor Flight Experiment of the F-LASDRA System 103 5.1 System Setup 103 5.2 Experiment Results 104 6 Conclusion 111 6.1 Summary 111 6.2 Future Works 112Docto