Design and implementation of a relative localization system for ground and aerial robotic teams

The main focus of this thesis is to address the relative localization problem of a heterogenous team which comprises of both ground and micro aerial vehicle robots. This team configuration allows to combine the advantages of increased accessibility and better perspective provided by aerial robots with the higher computational and sensory resources provided by the ground agents, to realize a cooperative multi robotic system suitable for hostile autonomous missions. However, in such a scenario, the strict constraints in flight time, sensor pay load, and computational capability of micro aerial vehicles limits the practical applicability of popular map-based localization schemes for GPS denied navigation. Therefore, the resource limited aerial platforms of this team demand simpler localization means for autonomous navigation. Relative localization is the process of estimating the formation of a robot team using the acquired inter-robot relative measurements. This allows the team members to know their relative formation even without a global localization reference, such as GPS or a map. Thus a typical robot team would benefit from a relative localization service since it would allow the team to implement formation control, collision avoidance, and supervisory control tasks, independent of a global localization service. More importantly, a heterogenous team such as ground robots and computationally constrained aerial vehicles would benefit from a relative localization service since it provides the crucial localization information required for autonomous operation of the weaker agents. This enables less capable robots to assume supportive roles and contribute to the more powerful robots executing the mission. Hence this study proposes a relative localization-based approach for ground and micro aerial vehicle cooperation, and develops inter-robot measurement, filtering, and distributed computing modules, necessary to realize the system. The research study results in three significant contributions. First, the work designs and validates a novel inter-robot relative measurement hardware solution which has accuracy, range, and scalability characteristics, necessary for relative localization. Second, the research work performs an analysis and design of a novel nonlinear filtering method, which allows the implementation of relative localization modules and attitude reference filters on low cost devices with optimal tuning parameters. Third, this work designs and validates a novel distributed relative localization approach, which harnesses the distributed computing capability of the team to minimize communication requirements, achieve consistent estimation, and enable efficient data correspondence within the network. The work validates the complete relative localization-based system through multiple indoor experiments and numerical simulations. The relative localization based navigation concept with its sensing, filtering, and distributed computing methods introduced in this thesis complements system limitations of a ground and micro aerial vehicle team, and also targets hostile environmental conditions. Thus the work constitutes an essential step towards realizing autonomous navigation of heterogenous teams in real world applications

A Continuous-Time Nonlinear Observer for Estimating Structure from Motion from Omnidirectional Optic Flow

Various insect species utilize certain types of self-motion to perceive structure in their local environment, a process known as active vision. This dissertation presents the development of a continuous-time formulated observer for estimating structure from motion that emulates the biological phenomenon of active vision. In an attempt to emulate the wide-field of view of compound eyes and neurophysiology of insects, the observer utilizes an omni-directional optic flow field. Exponential stability of the observer is assured provided the persistency of excitation condition is met. Persistency of excitation is assured by altering the direction of motion sufficiently quickly. An equal convergence rate on the entire viewable area can be achieved by executing certain prototypical maneuvers. Practical implementation of the observer is accomplished both in simulation and via an actual flying quadrotor testbed vehicle. Furthermore, this dissertation presents the vehicular implementation of a complimentary navigation methodology known as wide-field integration of the optic flow field. The implementation of the developed insect-inspired navigation methodologies on physical testbed vehicles utilized in this research required the development of many subsystems that comprise a control and navigation suite, including avionics development and state sensing, model development via system identification, feedback controller design, and state estimation strategies. These requisite subsystems and their development are discussed

Navigation and autonomy of soaring unmanned aerial vehicles

The use of Unmanned Aerial Vehicles (UAV) has exploded over the last decade with the constant need to reduce costs while maintaining capability. Despite the relentless development of electronics and battery technology there is a sustained need to reduce the size and weight of the on-board systems to free-up payload capacity. One method of reducing the energy storage requirement of UAVs is to utilise naturally occurring sources of energy found in the atmosphere. This thesis explores the use of static and semi-dynamic soaring to extract energy from naturally occurring shallow layer cumulus convection to improve range, endurance and average speed. A simulation model of an X-Models XCalibur electric motor-glider is used in combination with a refined 4D parametric atmospheric model to simulate soaring flight. The parametric atmospheric model builds on previous successful models with refinements to more accurately describe the weather in northern Europe. The implementation of the variation of the MacCready setting is discussed. Methods for generating efficient trajectories are evaluated and recommendations are made regarding implementation. For micro to small UAVs to be able to track the desired trajectories a highly accurate Attitude Heading Reference System (AHRS) is needed. Detailed analysis of the practical implementation of advanced attitude determination is used to enable optimal execution of the trajectories generated. The new attitude determination methods are compared to existing Kalman and complimentary type filters. Analysis shows the methods developed are capable of providing accurate attitude determination with extremely low computational requirements, even during extreme manoeuvring. The new AHRS techniques reduce the need for powerful on-board microprocessors. This new AHRS technique is used as a foundation to develop a robust navigation filter capable of providing improved drift performance, over traditional filters, in the temporary absence of global navigation satellite information. All these algorithms have been verified by flight tests using a mixture of manned and unmanned aerial vehicles and avionics developed specifically for this thesis

Cascaded complementary filter architecture for sensor fusion in attitude estimation

Copyright: © 2021 by the authors. Attitude estimation is the process of computing the orientation angles of an object with respect to a fixed frame of reference. Gyroscope, accelerometer, and magnetometer are some of the fundamental sensors used in attitude estimation. The orientation angles computed from these sensors are combined using the sensor fusion methodologies to obtain accurate estimates. The complementary filter is one of the widely adopted techniques whose performance is highly dependent on the appropriate selection of its gain parameters. This paper presents a novel cascaded architecture of the complementary filter that employs a nonlinear and linear version of the complementary filter within one framework. The nonlinear version is used to correct the gyroscope bias, while the linear version estimates the attitude angle. The significant advantage of the proposed architecture is its independence of the filter parameters, thereby avoiding tuning the filter’s gain parameters. The proposed architecture does not require any mathematical modeling of the system and is computationally inexpensive. The proposed methodology is applied to the real-world datasets, and the estimation results were found to be promising compared to the other state-of-the-art algorithms

Fusion of Imaging and Inertial Sensors for Navigation

The motivation of this research is to address the limitations of satellite-based navigation by fusing imaging and inertial systems. The research begins by rigorously describing the imaging and navigation problem and developing practical models of the sensors, then presenting a transformation technique to detect features within an image. Given a set of features, a statistical feature projection technique is developed which utilizes inertial measurements to predict vectors in the feature space between images. This coupling of the imaging and inertial sensors at a deep level is then used to aid the statistical feature matching function. The feature matches and inertial measurements are then used to estimate the navigation trajectory using an extended Kalman filter. After accomplishing a proper calibration, the image-aided inertial navigation algorithm is then tested using a combination of simulation and ground tests using both tactical and consumer- grade inertial sensors. While limitations of the Kalman filter are identified, the experimental results demonstrate a navigation performance improvement of at least two orders of magnitude over the respective inertial-only solutions

Vision-based control and autonomous landing of a VTOL-UAV

In recent years the popularity of quadrotor unmanned aerial vehicles (UAVs) has increased. Today, UAVs are widely used by military and police forces for surveillance. They are used by industry for such tasks as traffic monitoring, infrastructure inspection or even delivery of goods. They are used by individuals for hobby flying and aerial photography. It is currently of great interest in the research community to improve the level of autonomy of the UAV for these and future uses. One particular problem is the ability to stabilize over and land on a moving platform. This situation can easily arise for a quadrotor returning to a ship at sea or even a landing pad affixed to a vehicle. Many current techniques rely on knowledge of the platform and its motion, or a predictive model. This information is not always available or accurate. A solution that does not require knowledge of the target is desirable. This thesis deals with practical implementation of optical flow based position stabilization and autonomous landing algorithms for a quadrotor UAV. The quadrotor used is a common low cost platform with a large open source community. Firstly, non-linear estimation and control techniques are implemented for the attitude stabilization using low-cost sensors and limited computational power. Some methods for the system parameters estimation are presented and some challenges related to the implementation are discussed. Despite the ability of the attitude controller to stabilize the orientation of the quadrotor, hovering and landing precisely over a specific area is not possible without a position stabilization scheme. In applications where GPS signals are not available and the hovering target is a priori unknown, it is common to rely on visual information. In this context, this thesis aims for the development of an efficient optical-flow-based position stabilization and autonomous landing scheme for the quadrotor UAV

Real-time implementation of some attitude estimation algorithms on a quadrotor UAV / by Siddhant Nayak.

The recent developments in research pertaining to the field of Unmanned Aerial Vehicles (UAVs) is motivated by its technical challenges as well as its practical implications in areas where human presence is inefficient, redundant or dangerous. The absence of human interference requires more robust and precise control techniques. However, most modern attitude control techniques require the knowledge of the current orientation of the body. There is no sensor available that explicitly measures the attitude of a rigid body and hence, for small scale UAVs. it must be estimated using inertial vector measurements from low-cost and low-weight Micro-Electro-Mechanical System (MEMS) sensors like gyroscopes, accelerometers and magnetometers. The predominant attitude representation formulations of a rigid body in three-dimensional space are recapitulated to elucidate the dynamical model of a quadrotor UAV. Low-cost MEMS are prone to significant noise effects from temperature change, vibrations, on-board magnetic fields generated by motors and currents. To improve the accuracy of the measurements sensor calibration techniques are explored. Primitive attitude estimation techniques like TRIAD, Davenports q-method, QUEST.FOAM, SVD method, etc. (which were aimed to be static optimization solutions to Wahbas Problem) were reviewed. These algorithms were extended to incorporate filtering techniques like Kahnan-type, to handle the measurement noise, and complementary filtering, where sensor measurements are fused to reconstruct the orientation of a rigid body. Tlie latest nonlinear observers are also discussed for implementation purposes. Practical implementation and performance comparison of various attitude estimation algorithms has been conducted on a small-scale quadrotor UAV, consisting of an inertial measurement unit (3-axis gyroscope, accelerometer and magnetometer), microcontroller, brushless motors, electronic speed controllers, on-board power supply and necessary frame constructs

Guidance, Navigation and Control for UAV Close Formation Flight and Airborne Docking

Unmanned aerial vehicle (UAV) capability is currently limited by the amount of energy that can be stored onboard or the small amount that can be gathered from the environment. This has historically lead to large, expensive vehicles with considerable fuel capacity. Airborne docking, for aerial refueling, is a viable solution that has been proven through decades of implementation with manned aircraft, but had not been successfully tested or demonstrated with UAVs. The prohibitive challenge is the highly accurate and reliable relative positioning performance that is required to dock with a small target, in the air, amidst external disturbances. GNSS-based navigation systems are well suited for reliable absolute positioning, but fall short for accurate relative positioning. Direct, relative sensor measurements are precise, but can be unreliable in dynamic environments. This work proposes an experimentally verified guidance, navigation and control solution that enables a UAV to autonomously rendezvous and dock with a drogue that is being towed by another autonomous UAV. A nonlinear estimation framework uses precise air-to-air visual observations to correct onboard sensor measurements and produce an accurate relative state estimate. The state of the drogue is estimated using known geometric and inertial characteristics and air-to-air observations. Setpoint augmentation algorithms compensate for leader turn dynamics during formation flight, and drogue physical constraints during docking. Vision-aided close formation flight has been demonstrated over extended periods; as close as 4 m; in wind speeds in excess of 25 km/h; and at altitudes as low as 15 m. Docking flight tests achieved numerous airborne connections over multiple flights, including five successful docking manoeuvres in seven minutes of a single flight. To the best of our knowledge, these are the closest formation flights performed outdoors and the first UAV airborne docking

Precision autonomous underwater navigation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 175-185).Deep-sea archaeology, an emerging application of autonomous underwater vehicle (AUV) technology, requires precise navigation and guidance. As science requirements and engineering capabilities converge, navigating in the sensor-limited ocean remains a fundamental challenge. Despite the logistical cost, the standards of archaeological survey necessitate using fixed acoustic transponders - an instrumented navigation environment. This thesis focuses on the problems particular to operating precisely within such an environment by developing a design method and a navigation algorithm. Responsible documentation, through remote sensing images, distinguishes archaeology from salvage, and fine-resolution imaging demands precision navigation. This thesis presents a design process for making component and algorithm level tradeoffs to achieve system-level performance satisfying the archaeological standard. A specification connects the functional requirements of archaeological survey with the design parameters of precision navigation. Tools based on estimation fundamentals - the Cram6r-Rao lower bound and the extended Kalman filter - predict the system-level precision of candidate designs. Non-dimensional performance metrics generalize the analysis results. Analyzing a variety of factors and levels articulates the key tradeoffs: sensor selection, acoustic beacon configuration, algorithm selection, etc. The abstract analysis is made concrete by designing a survey and navigation system for an expedition to image the USS Monitor. Hypothesis grid (Hgrid) is both a representation of the sensed environment and an algorithm for building the representation. Range observations measuring the line-of-sight distance between two acoustic transducers are subject to multipath errors and spurious returns.The quality of this measurement is dependent on the location of the estimator. Hgrids characterize the measurement quality by generating a priori association probabilities - the belief that subsequent measurements will correspond to the direct-path, a multipath, or an outlier - as a function of the estimated location. The algorithm has three main components: the mixed-density sensor model using Gaussian and uniform probability distributions, the measurement classification and multipath model identification using expectation-maximization (EM), and the grid-based spatial representation. Application to data from an autonomous benthic explorer (ABE) dive illustrates the algorithm and shows the feasibility of the approach.by Brian Steven Bingham.Ph.D

Robust airborne 3D visual simultaneous localisation and mapping

The aim of this thesis is to present robust solutions to technical problems of airborne three-dimensional (3D) Visual Simultaneous Localisation And Mapping (VSLAM). These solutions are developed based on a stereovision system available onboard Unmanned Aerial Vehicles (UAVs). The proposed airborne VSLAM enables unmanned aerial vehicles to construct a reliable map of an unknown environment and localise themselves within this map without any user intervention. Current research challenges related to Airborne VSLAM include the visual processing through invariant feature detectors/descriptors, efficient mapping of large environments and cooperative navigation and mapping of complex environments. Most of these challenges require scalable representations, robust data association algorithms, consistent estimation techniques, and fusion of different sensor modalities. To deal with these challenges, seven Chapters are presented in this thesis as follows: Chapter 1 introduces UAVs, definitions, current challenges and different applications. Next, in Chapter 2 we present the main sensors used by UAVs during navigation. Chapter 3 presents an important task for autonomous navigation which is UAV localisation. In this chapter, some robust and optimal approaches for data fusion are proposed with performance analysis. After that, UAV map building is presented in Chapter 4. This latter is divided into three parts. In the first part, a new imaging alternative technique is proposed to extract and match a suitable number of invariant features. The second part presents an image mosaicing algorithm followed by a super-resolution approach. In the third part, we propose a new feature detector and descriptor that is fast, robust and detect suitable number of features to solve the VSLAM problem. A complete Airborne Visual Simultaneous Localisation and Mapping (VSLAM) solution based on a stereovision system is presented in Chapter (5). Robust data association filters with consistency and observability analysis are presented in this chapter as well. The proposed algorithm is validated with loop closing detection and map management using experimental data. The airborne VSLAM is extended then to the multiple UAVs case in Chapter (6). This chapter presents two architectures of cooperation: a Centralised and a Decentralised. The former provides optimal precision in terms of UAV positions and constructed map while the latter is more suitable for real time and embedded system applications. Finally, conclusions and future works are presented in Chapter (7).EThOS - Electronic Theses Online ServiceGBUnited Kingdo