A-TIRMA: A small autonomous sailboat

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Valutazione dell'efficacia delle "noise abatement procedures": il caso dell'aeroporto G. Marconi di Bologna

A partire dalle caratteristiche chiave dell’inquinamento acustico, lo scopo della tesi è stato quello di valutare quantitativamente l’entità del rumore aeronautico prodotto dall'aeroporto Marconi e di analizzare le soluzioni tecniche e gestionali disponibili per definire misure adeguate alle caratteristiche infrastrutturali e operative dello scalo e capaci di mitigare il disturbo subito dalla popolazione. Si è tenuto conto delle profonde modificazioni in atto nel mondo dell’aviazione, il quale, avendo come obiettivo quello di fornire un servizio di trasporto sempre più sostenibile, efficace, competitivo e omogeneo sul territorio europeo, sollecita profonde innovazioni nei requisiti funzionali e tecnici. Inizialmente l’attenzione è stata rivolta alla descrizione del rumore aeronautico e del contesto in cui è inserito, soffermandosi sul concetto di sostenibilità di un’infrastruttura di trasporto. Si è proseguito con un'analisi dettagliata della normativa vigente, italiana ed europea, al fine di affrontare gli aspetti legislativi del problema e di delineare le line guida per la valutazione del rumore. Segue uno studio, dal punto di vista tecnico e infrastrutturale, dell’evoluzione della navigazione aerea e del concetto innovativo di performance based navigation, focalizzando l’interesse sul curved approach, procedura di avvicinamento non convenzionale. L'attenzione è stata, poi, dedicata alla descrizione del caso di studio e alla presentazione della metodologia usata. Mediante il supporto dell’INM, sono state determinate le curve isofoniche, quantificando la popolazione esposta a specifici livelli di rumore aeronautico per lo scenario consuntivo dell’anno 2015. Infine, sono state eseguite simulazioni future, sulla base delle previsioni di crescita del volume di traffico aereo, per definire un limite massimo per lo sfruttamento del sistema ILS in testata 30 e per valutare il beneficio generato dall’introduzione del curved approach.

Percutaneous radiofrequency ablation of virtual tumours in canine kidney using Global Positioning System-like technology

What's known on the subject? And what does the study add? We have previously shown that percutaneous radiofrequency ablation guided by image-fusion technology allows for precise needle placement with real time ultrasound superimposed with pre-loaded imaging, removing the need for real-time CT or MR guidance. Emerging technology also allows real-time tracking of a treatment needle within an organ in a virtually created 3D format. To our knowledge, this is the first study utilising a sophisticated ultrasound-based navigation system that uses both image-fusion and real-time probe-tracking technologies for in-vivo renal ablative intervention.

Accuracy of navigated surgery of the pelvis after surface matching with an a-mode ultrasound probe

Computer-aided surgery (CAS) allows for real-time intraoperative feedback resulting in increased accuracy, while reducing intraoperative radiation. CAS is especially useful for the treatment of certain pelvic ring fractures, which necessitate the precise placement of screws. Flouroscopy-based CAS modules have been developed for many orthopedic applications. The integration of the isocentric flouroscope even enables navigation using intraoperatively acquired three-dimensional (3D) data, though the scan volume and imaging quality are limited. Complicated and comprehensive pathologies in regions like the pelvis can necessitate a CT-based navigation system because of its larger field of view. To be accurate, the patient's anatomy must be registered and matched with the virtual object (CT data). The actual precision within the region of interest depends on the area of the bone where surface matching is performed. Conventional surface matching with a solid pointer requires extensive soft tissue dissection. This contradicts the primary purpose of CAS as a minimally invasive alternative to conventional surgical techniques. We therefore integrated an a-mode ultrasound pointer into the process of surface matching for pelvic surgery and compared it to the conventional method. Accuracy measurements were made in two pelvic models: a foam model submerged in water and one with attached porcine muscle tissue. Three different tissue depths were selected based on CT scans of 30 human pelves. The ultrasound pointer allowed for registration of virtually any point on the pelvis. This method of surface matching could be successfully integrated into CAS of the pelvis.

Comparison of freehand-navigated and aiming device-navigated targeting of liver lesions

BACKGROUND Accurate needle placement is crucial for the success of percutaneous radiological needle interventions. We compared three guiding methods using an optical-based navigation system: freehand, using a stereotactic aiming device and active depth control, and using a stereotactic aiming device and passive depth control. METHODS For each method, 25 punctures were performed on a non-rigid phantom. Five 1 mm metal screws were used as targets. Time requirements were recorded, and target positioning errors (TPE) were measured on control scans as the distance between needle tip and target. RESULTS Time requirements were reduced using the aiming device and passive depth control. The Euclidian TPE was similar for each method (4.6 ± 1.2-4.9 ± 1.7 mm). However, the lateral component was significantly lower when an aiming device was used (2.3 ± 1.3-2.8 ± 1.6 mm with an aiming device vs 4.2 ± 2.0 mm without). DISCUSSION Using an aiming device may increase the lateral accuracy of navigated needle insertion.

Traffic sign detection and tracking using robust 3D analysis

In this paper we present an innovative technique to tackle the problem of automatic road sign detection and tracking using an on-board stereo camera. It involves a continuous 3D analysis of the road sign during the whole tracking process. Firstly, a color and appearance based model is applied to generate road sign candidates in both stereo images. A sparse disparity map between the left and right images is then created for each candidate by using contour-based and SURF-based matching in the far and short range, respectively. Once the map has been computed, the correspondences are back-projected to generate a cloud of 3D points, and the best-fit plane is computed through RANSAC, ensuring robustness to outliers. Temporal consistency is enforced by means of a Kalman filter, which exploits the intrinsic smoothness of the 3D camera motion in traffic environments. Additionally, the estimation of the plane allows to correct deformations due to perspective, thus easing further sign classification.

Localización y seguimiento de aeronaves mediante sistemas de multilateración de área extensa

El continuo crecimiento de la demanda del transporte aéreo, junto con los nuevos escenarios de intervención militar, están obligando a una optimización en el uso del espacio aéreo. De este modo, la UE y los EEUU (a través de SESAR y NextGen respectivamente) han asentado las bases para una nueva gestión del tráfico aéreo (ATM). Con ello, se pretende aumentar la capacidad de aeropuertos y rutas aéreas, otorgando mayor flexibilidad al uso del espacio aéreo sin comprometer la seguridad de los usuarios. Desde un punto de vista puramente técnico, la clave de este cambio de modelo está en el conocimiento de la posición de cada aeronave en cada instante. En este sentido, la tendencia en ATM es el uso de ADS-B como fuente principal de posicionamiento. Sin embargo, debido a que este sistema está basado en la difusión de la posición obtenida a través de GPS, es necesario un sistema de seguimiento independiente. Actualmente, la intención es migrar del radar secundario de vigilancia (SSR) a la multilateración de área extensa (WAM), con el fin de mejorar la integridad de la posición para aplicaciones en ruta. Aprovechando el rápido despliegue de ADS-B, se pretende reutilizar sus estaciones base para WAM. Cada estación base que recibe el mensaje ADS-B de la aeronave envía conjuntamente la medida del tiempo de llegada (TOA) de dicho mensaje al centro de tráfico aéreo. La posición de la aeronave se obtiene mediante multilateración, cuya técnica consiste en utilizar las medidas de TOA de un mismo mensaje ADS-B obtenidas en las distintas estaciones base. El objetivo es estimar la posición de cada aeronave con la mayor precisión posible. Para poder diseñar el sistema que permite alcanzar este objetivo, son dos los aspectos básicos a estudiar. Por una parte, la identificación y posterior caracterización de los errores (tanto sistemáticos como aleatorios) que afectan a la medida de TOA. Por otra parte, es necesario el estudio de los sistemas de seguimiento, basados en versiones sofisticadas del filtro de Kalman (IMM, UKF). Una vez establecidos estos dos pilares, la presente tesis doctoral propone un sistema que permite efectuar el seguimiento de las aeronaves, corrigiendo los efectos de las principales distorsiones que afectan a la medida de TOA: la refracción troposférica y el error de sincronismo. La mejora en la precisión de la localización ha sido evaluada mediante simulación de escenarios hipotéticos. ABSTRACT The ever-growing demand in the air transportation and the new military intervention scenarios, are generating a need to optimize the use of the airspace. This way, the EU and the USA (through SESAR and NextGen respectively) have set the ground to overhaul the current air traffic management. The intention is to enhance the capacity of airports and air routes, providing greater flexibility in the use of airspace without jeopardizing the security of the end-users. From a technical perspective, the key for this change lies in the knowledge of the aircraft position. The trend in Air Traffic Management (ATM) is to rely on ADS-B as the main source for aircraft positioning. However, this system is based on the aircraft’s self-declaration of its own (often GPS-based) navigation solution. It is therefore necessary to have an independent surveillance system. Nowadays, the intention is to gradually migrate from Secondary Surveillance Radar (SSR) towards Wide Area Multilateration (WAM) in order to enhance surveillance integrity for en-route applications. Given the fast deployment of ADS-B, the aim is to use its base stations for WAM. Each station sends the Time of Arrival (TOA) of the received ADS-B messages to the air traffic center (ATC). The aircraft position is obtained through multilateration, using the TOA of the same message measured by each station. The aim is to accurately estimate the position of each aircraft. Knowledge from two key areas has to be gathered prior to designing such a system. It is necessary to identify and then characterize the errors (both systematic and random) affecting the TOA measurements. The second element is the study of tracking systems based on sophisticated versions of the Kalman filtering (e.g. IMM, UKF). Based on this knowledge, the main contribution of this Ph.D. is an aircraft tracking system that corrects the effects of the main errors involved in the TOA measurement: tropospheric refraction and synchronization issues. Performance gains in positioning accuracy have been assessed by simulating hypothetical WAM scenarios.

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.

Vision based guidance for robot navigation in agriculture

This paper describes a novel vision based texture tracking method to guide autonomous vehicles in agricultural fields where the crop rows are challenging to detect. Existing methods require sufficient visual difference between the crop and soil for segmentation, or explicit knowledge of the structure of the crop rows. This method works by extracting and tracking the direction and lateral offset of the dominant parallel texture in a simulated overhead view of the scene and hence abstracts away crop-specific details such as colour, spacing and periodicity. The results demonstrate that the method is able to track crop rows across fields with extremely varied appearance during day and night. We demonstrate this method can autonomously guide a robot along the crop rows.

Automated road pavement marking detection from high resolution aerial images based on Multi-resolution image analysis and anisotropic Gaussian filtering

Road features extraction from remote sensed imagery has been a long-term topic of great interest within the photogrammetry and remote sensing communities for over three decades. The majority of the early work only focused on linear feature detection approaches, with restrictive assumption on image resolution and road appearance. The widely available of high resolution digital aerial images makes it possible to extract sub-road features, e.g. road pavement markings. In this paper, we will focus on the automatic extraction of road lane markings, which are required by various lane-based vehicle applications, such as, autonomous vehicle navigation, and lane departure warning. The proposed approach consists of three phases: i) road centerline extraction from low resolution image, ii) road surface detection in the original image, and iii) pavement marking extraction on the generated road surface. The proposed method was tested on the aerial imagery dataset of the Bruce Highway, Queensland, and the results demonstrate the efficiency of our approach.

Vision-based topological mapping and localisation

Competent navigation in an environment is a major requirement for an autonomous mobile robot to accomplish its mission. Nowadays, many successful systems for navigating a mobile robot use an internal map which represents the environment in a detailed geometric manner. However, building, maintaining and using such environment maps for navigation is difficult because of perceptual aliasing and measurement noise. Moreover, geometric maps require the processing of huge amounts of data which is computationally expensive. This thesis addresses the problem of vision-based topological mapping and localisation for mobile robot navigation. Topological maps are concise and graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well-suited for basic robot navigation applications, and also provide a representational basis for the procedural and semantic information needed for higher-level robotic tasks. In order to make vision-based topological navigation suitable for inexpensive mobile robots for the mass market we propose to characterise key places of the environment based on their visual appearance through colour histograms. The approach for representing places using visual appearance is based on the fact that colour histograms change slowly as the field of vision sweeps the scene when a robot moves through an environment. Hence, a place represents a region of the environment rather than a single position. We demonstrate in experiments using an indoor data set, that a topological map in which places are characterised using visual appearance augmented with metric clues provides sufficient information to perform continuous metric localisation which is robust to the kidnapped robot problem. Many topological mapping methods build a topological map by clustering visual observations to places. However, due to perceptual aliasing observations from different places may be mapped to the same place representative in the topological map. A main contribution of this thesis is a novel approach for dealing with the perceptual aliasing problem in topological mapping. We propose to incorporate neighbourhood relations for disambiguating places which otherwise are indistinguishable. We present a constraint based stochastic local search method which integrates the approach for place disambiguation in order to induce a topological map. Experiments show that the proposed method is capable of mapping environments with a high degree of perceptual aliasing, and that a small map is found quickly. Moreover, the method of using neighbourhood information for place disambiguation is integrated into a framework for topological off-line simultaneous localisation and mapping which does not require an initial categorisation of visual observations. Experiments on an indoor data set demonstrate the suitability of our method to reliably localise the robot while building a topological map.

The RatSLAM project : robot spatial navigation

Rats are superior to the most advanced robots when it comes to creating and exploiting spatial representations. A wild rat can have a foraging range of hundreds of meters, possibly kilometers, and yet the rodent can unerringly return to its home after each foraging mission, and return to profitable foraging locations at a later date (Davis, et al., 1948). The rat runs through undergrowth and pipes with few distal landmarks, along paths where the visual, textural, and olfactory appearance constantly change (Hardy and Taylor, 1980; Recht, 1988). Despite these challenges the rat builds, maintains, and exploits internal representations of large areas of the real world throughout its two to three year lifetime. While algorithms exist that allow robots to build maps, the questions of how to maintain those maps and how to handle change in appearance over time remain open. The robotic approach to map building has been dominated by algorithms that optimise the geometry of the map based on measurements of distances to features. In a robotic approach, measurements of distance to features are taken with range-measuring devices such as laser range finders or ultrasound sensors, and in some cases estimates of depth from visual information. The features are incorporated into the map based on previous readings of other features in view and estimates of self-motion. The algorithms explicitly model the uncertainty in measurements of range and the measurement of self-motion, and use probability theory to find optimal solutions for the geometric configuration of the map features (Dissanayake, et al., 2001; Thrun and Leonard, 2008). Some of the results from the application of these algorithms have been impressive, ranging from three-dimensional maps of large urban strucutures (Thrun and Montemerlo, 2006) to natural environments (Montemerlo, et al., 2003).

Image-based visual navigation for mobile robots

We introduce a new image-based visual navigation algorithm that allows the Cartesian velocity of a robot to be defined with respect to a set of visually observed features corresponding to previously unseen and unmapped world points. The technique is well suited to mobile robot tasks such as moving along a road or flying over the ground. We describe the algorithm in general form and present detailed simulation results for an aerial robot scenario using a spherical camera and a wide angle perspective camera, and present experimental results for a mobile ground robot.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online.