Orthopaedic residency training is in the midst of a paradigm shift. Recent mandates from the Residency Review Committee (RRC) for Orthopaedic Surgery and the American Board of Orthopaedic Surgery (ABOS) are requiring that programs must provide structured motor skills training to first year residents. Although other surgical fields such as laparoscopic surgery have been using simulation tools to train incoming residents for over a decade, the orthopaedic field has lagged behind in developing these training tools. Given the need for orthopaedic training devices and the lack of currently available solutions to residency programs, this work has focused on developing a surgical simulator for the task of hip guide wire navigation. Hip wire navigation was targeted for this work because it is a core competency skill for surgical residents and few options currently exist for training residents on this task. Much of this work focuses on the development of the wire navigation simulator. The simulator has six main components; a single camera interfaced with a Raspberry Pi (a credit-card sized computer), a series of three mirrors, a surrogate femur, a guide wire driver, a laser etched guide wire, and a laptop. These components interact to create virtual radiograph images that the resident can use to place the guide wire inside the bone. The goal in developing this simulator is to provide a platform which enables residents to acquire the skill of hip wire navigation in a safe environment and eventually transfer that skill into the operating room. Assessment of the simulator has shown that the guide wire can be located in bone within 1.5mm of its true position and less than a degree of its true trajectory. This level of accuracy is sufficient for providing residents with a training tool to practice their technique on. In training with resident surgeons, initial trends show that practicing with the simulator can result in an improvement in one’s technique. Residents who have trained with the simulator show a decrease in both the amount of radiographic images required to complete the procedure and the amount of time required to perform the procedure in a pseudo operating room environment. While more work is needed to be done to show the significance of this trend, this work has achieved its goal of providing residents with a safe platform for practicing the task of hip guide wire navigation.

Unmanned vehicles are increasingly common in our world today. Self-driving ground vehicles and unmanned aerial vehicles (UAVs) such as quadcopters have become the fastest growing area of automated vehicles research. These systems use three main processes to autonomously travel from one location to another: guidance, navigation, and controls (GNC). Guidance refers to the process of determining a desired path of travel or trajectory, affecting velocities and orientations. Examples of guidance activities include path planning and obstacle avoidance. Effective guidance decisions require knowledge of one’s current location. Navigation systems typically answer questions such as: “Where am I? What is my orientation? How fast am I going?” Finally, the process is tied together when controls are implemented. Controls use navigation estimates (e.g., “Where I am now?”) and the desired trajectory from guidance processes (e.g., “Where do I want to be?”) to control the moving parts of the system to accomplish relevant goals. Navigation in autonomous vehicles involves intelligently combining information from several sensors to produce accurate state estimations. To date, global positioning systems(GPS) occupy a crucial place in most navigation systems. However, GPS is not universally reliable. Even when available, GPS can be easily spoofed or jammed, rendering it useless. Thus, navigation within GPS-denied environments is an area of deep interest in both military and civilian applications. Image-aided inertial navigation is an alternative navigational solution in GPS-denied environments. One form of image-aided navigation measures the bearing from the vehicle to a feature or landmark of known location using a single lens imager, such as a camera, to deduce information about the vehicle’s position and attitude. This work uncovers and explores several of the impacts of trajectories and land mark distributions on the navigation information gained from this type of aiding measurement. To do so, a modular system model and extended Kalman filter (EKF) are described and implemented. A quadrotor system model is first presented. This model is implemented and then used to produce sensor data for several trajectories of varying shape, altitude, and landmark density. Next, navigation data is produced by running the sensor data through an EKF. The data is plotted and examined to determine effects of each variable. These effects are then explained. Finally, an equation describing the quantity of information in each measurement is derived and related to the patterns seen in the data. The resulting equation is then used to explain selected patterns in the data. Other uses of this equation are presented, including applications to path planning and landmark placement.

Navigation and guidance of mobile robots towards steady or maneuvering objects (targets) is one of the most important areas of robotics that has attracted a lot of attention in recent decades. However, in most of the existing methods, both the line-of-sight angle (bearing) and the relative distance (range) are assumed to be available for navigation and guidance algorithms. There is also a relatively large body of research on navigation and guidance with bearings-only measurements. In contrast, only a few results on navigation and guidance towards an unknown target using range-only measurements have been published. Various problems of navigation, guidance, location estimation and target tracking based on range-only measurements often arise in new wireless networks related applications. Recent advances in these applications allow us to use inexpensive transponders and receivers for range-only measurements which provide information in dynamic and noisy environments without the necessity of line-of-sight. To take advantage of these sensors, algorithms must be developed for range-only navigation. The main part of this thesis is concerned with the problem of real-time navigation and guidance of Wheeled Mobile Robots (WMRs) towards an unknown stationary or moving target using range-only measurements. The range can be estimated using the signal strength and the robust extended Kalman filtering. Several similar algorithms for navigation and guidance termed Equiangular Navigation and Guidance (ENG) laws are proposed and mathematically rigorous proofs of convergence and stability of the proposed guidance laws are given. The experimental investigation into the use of range data for a WMR navigation is documented and the results and discussions on the performance of the proposed guidance strategies are presented, where a wheeled robot successfully approach a stationary or follow a maneuvering target. In order to safely navigate and reliably operate in populated environments, ENG is then modified into Augmented-ENG (AENG), which enables the robot to approach a stationary target or follow an unpredictable maneuvering object in an unknown environment, while keeping a safe distance from the target, and simultaneously preserving a safety margin from the obstacles. Furthermore, we propose and experimentally investigate a new biologically inspired method for local obstacle avoidance and give the mathematically rigorous proof of the idea. In order for the robot to avoid collision and bypass the enroute obstacles in this method, the angle between the instantaneous moving direction of the robot and a reference point on the surface of the obstacle is kept constant. The proposed idea is combined with the ENG law, which leads to a reliable and fast long-range navigation. The performance of both navigation strategy and local obstacle avoidance techniques are confirmed with computer simulations and several experiments with ActivMedia Pioneer 3-DX wheeled robots. The second part of the thesis investigates some challenging problems in the area of wheeled robot navigation. We first address the problem of bearing-only guidance of an autonomous vehicle following a moving target with smaller minimum turning radius compared to that of the follower and propose a simple and constructive navigation law. In compliance with the increasing research on decentralized control laws for groups of mobile autonomous robots, we consider the problems of decentralized navigation of network of WMRs with limited communication and decentralized stabilization of formation of WMRs. New control laws are presented and simulation results are provided to illustrate the control laws and their applications.

The availability of GPS signals is a major limitation for many existing and potential applications. Fortunately, with the development of Galileo by the European Commission (EC) and European Space Agency (ESA) and new funding for the restoration of the Russian GLONASS announced by the Russian Federation the future for satellite based positioning and navigation applications is extremely promising. This research primarily investigates the benefits of GNSS interoperability and GNSS/INS integration to Receiver Autonomous Integrity Monitoring (RAIM) from a geometrical perspective. In addition to these investigations, issues regarding multiple outlier detection and identification are examined and integrity procedures addressing these issues are proposed. Moreover, it has been shown how the same RAIM algorithms can be effectively applied to the various static and kinematic navigation architectures used in this research.

By the year 2020 the number of aircraft will have increased substantially and will be in �Free Flight�(that is, ATC will be devolved to the aircraft rather than being ground based). As an aid to navigation a more advanced form of collision avoidance will be required. This thesis proposes a method of collision avoidance planning using Automatic Dependent Surveillance-Broadcast (ADS-B) and Dynamic Programming (DP). It in essence enables Air Traffic Control (ATC) from within the cockpit for remote or uncontrolled airspace and is a step toward Free Flight. Free Flight requires quite different strategies than those used in the present collision avoidance schemes. This thesis reviews the approaches to collision avoidance used in the Air traffic navigation and to similar problems in other industries. In particular it considers the extended problem of collision avoidance within the framework of path planning. This is a key departure from the approach to aircraft collision avoidance used in the industry to date. Path planning reflects the real goal of an aircraft, which is to reach a particular destination efficiently and safely. Dynamic Programming is one solution method used in other industries for the problem of path planning to avoid collisions with fixed obstacles. The solution proposed herein for the Aircraft case uses Dynamic Programming applied to the moving obstacle case. The problem is first simplified by assuming fixed (static) obstacles for the cost minimisation algorithms. These fixed obstacles are then moved with time and the minimisation process is repeated at each time increment. Although this method works well in most cases, situations can be constructed where this method fails, allowing a collision. A modified approach is then used, whereby the movement of obstacles is included more explicitly (by modifying the shapes of the obstacles to represent motion) in the cost minimisation algorithm and a safe manoeuvre distance for each aircraft is used (by expanding the object size), to allow space for aircraft to execute safe evasive manoeuvres in difficult cases. This modification allows solutions which are complete (with no known cases of failure � collision situations) and should be considered as an important extension to the current Aircraft and Collision Avoidance System (ACAS). The testing of these solutions is focussed on the most difficult cases, and includes aircraft movement in �real space� (that is simulations using real aircraft dynamics together with dynamic programming algorithms running in discrete time steps).

La navigation autonome d'un robot requiert l'intégration des trois grandes familles de processus de perception, de décision et d'action, intégration qui caractérise les machines intelligentes. Les représentations de l'environnement que le robot doit construire par ses propres moyens sont au coeur de cette intégration : elles sont produites par les fonctions de perception, mais sont surtout exploitées pour la décision et l'action. Dans le contexte de robots en environnements extérieurs, structurés ou non, la construction autonome de telles représentations constitue encore un vaste domaine de recherches, qui requiert l'intégration à bord des robots de processus de traitement du signal et des images, de modélisation géométrique, et d'estimation stochatisques. Le problème de la localisation du robot dans son environnement y joue bien sûr un rôle majeur, car il est indissociable de la construction des modèles de l'environnement. L'exposé présentera différentes avancées qui ont été réalisées dans ce domaine, supportées par des démonstrations réalisées avec des robots terrestres et un ballon dirigeable.

Nous avons mené des travaux de recherche principalement dans les domaines de la navigation, la perception et l'apprentissage pour la robotique mobile. Ces travaux, orientés vers une robotique " cognitive ", ont pour objectif général de per- mettre aux robots de s'adapter à leur environnement, en fournissant les primitives de base telles que l'espace libre, la position ou la présence d'objets nécessaires au choix des actions. Une grande partie de ces travaux sont guidés par une inspi- ration biologique essentiellement fonctionnelle, s'inspirant de capacités trouvées dans la nature, sans chercher à en reproduire précisément le fonctionnement. La navigation, plus particulièrement la cartographie, a été jusqu'à présent le thème principal de nos travaux. Durant notre thèse, nous avons développé une méthode de cartographie utilisant un filtre bayésien, appliqué à une structure de carte et à des perceptions inspirées des connaissances biologiques sur les capacités de navigation du rat. L'intérêt de cette approche est de permettre, à partir de capteurs très simples, une localisation globale durant la cartographie, apportant ainsi une bonne robustesse à la navigation, au prix d'une exploration relativement lente. Cette inspiration biologique s'est ensuite effacée dans les tra- vaux menés à la Direction Générale pour l'Armement ou nous avons participé à la mise en place d'un démonstrateur utilisant des techniques de cartographie classiques à base de télémétrie laser et d'évitement d'obstacles en environnement dynamique. Depuis 2005 à l'ENSTA ParisTech, nos travaux se sont orientés sur les problèmes de navigation topologique, avec une approche de navigation topo- logique par apprentissage dans laquelle un utilisateur désigne les pièces à re- connaître et montre le chemin entre les différentes pièces. Nous avons également développé une approche de cartographie topologique utilisant un algorithme de détection de fermetures de boucles qui permet de détecter le retour d'un robot à une position connue. Enfin, ces travaux se sont maintenant étendus, depuis 2009 dans le cadre du projet ANR PACOM, à la problématique de la cartographie sé- mantique. L'objectif est d'obtenir des modèles de l'environnement contenant des informations de plus haut niveau; en particulier des informations plus proches de celles utilisées par l'humain, telles que les pièces ou les objets présents dans l'environnement. Au niveau de la perception, certains de ces travaux ont fait appel à la télémétrie laser, qui est bien adaptée à la navigation. Ils sont néanmoins axés principalement sur l'utilisation de la vision. En particulier, nous nous sommes intéressés au problème de la représentation de l'information visuelle qui est essentielle pour apporter une robustesse au bruit tout en fournissant l'information nécessaire aux applications. Nous avons ainsi développé une approche incrémentale inspirée des modèles de " sac de mots " visuels que nous avons appliqué à la localisation qualitative, topologique, au guidage visuel et à la reconnaissance d'objets. En collaboration avec Pierre-Yves Oudeyer nous avons étendu cette représentation à la reconnaissance auditive et audio-visuelle. Nous nous sommes également intéressés au problème de la perception active afin d'améliorer les capacités de localisation et d'améliorer la robustesse du guidage visuel. Enfin, la plupart de ces travaux ont fait appel à des méthodes d'apprentissage pour apporter de l'adaptabilité à la localisation, à la cartographie ou à la re- connaissance d'objets. Nous avons principalement travaillé avec des méthodes Bayésiennes et nous avons notamment développé des méthodes actives, permet- tant au robot de sélectionner les exemples d'apprentissage pour améliorer ses performances. Ces méthodes permettent également de profiter d'interactions avec l'utilisateur pour adapter les concepts appris par le robot. Nous les avons appli- quées à la reconnaissance de pièces et d'objets. Nous avons également appliqué la technique de factorisation en matrices non-négatives, une méthode d'appren- tissage non supervisée, pour la reconnaissance audio-visuelle d'objets. Cette der- nière application se place dans le cadre de la robotique développementale où nous cherchons à nous inspirer de l'homme pour créer des méthodes d'apprentis- sage intuitives et à long terme pour la robotique, approche que nous développons actuellement dans le cadre du projet ANR MACSi. Dans la continuité de ces travaux, nous souhaitons poursuivre nos recherches sur le thème de la navigation sémantique et de l'apprentissage pour la percep- tion dans le cadre de la robotique développementale. Ces recherches auront pour objectif commun de fournir au robot ou à son utilisateur des modèles d'environ- nement riches et contenant des informations utiles à l'analyse de la situation ou aux tâches du robot. Ces méthodes s'appliqueront essentiellement dans le cadre de la navigation en milieu intérieur ou urbain et à la robotique de service ou d'assistance, en interaction directe avec l'homme.

<p>This thesis describes an ongoing project of which the purpose is designing and developing techniques and algorithms for autonomous off-road vehicles. The focus is on some of the components necessary to accomplish autonomous navigation, which involves sensing and moving safely along a user-defined path in a dynamic forest environment. The work is part of a long-term vision in the forest industry of developing an unmanned shuttle that transports timber from the felling area to the main roads for further transportation. A new path-tracking algorithm is introduced and demonstrated as superior to standard algorithms, such as Follow the Carrot and Pure Pursuit. This is accomplished by using recorded data from a path-learning phase. By using the recorded steering angle, the curvature of the path is automatically included in the final steering command. Localization is accomplished by a neural network that fuses data from a Real-Time Kinematic Differential GPS/GLONASS, a gyro, and wheel odometry. Test results are presented for path tracking and localization accuracy from runs conducted on a full-sized forest machine. A large part of the work has been design and implementation of a general software platform for research in autonomous vehicles. The developed algorithms and software have been implemented and tested on a full-size forest machine supplied by our industrial partner Komatsu Forest AB. Results from successful field tests with autonomous path tracking, including obstacle avoidance, are presented.</p>

<p>In this paper we examine the need of navigational aids when designing interfaces for e-newspapers </p><p>– newspapers presented on a digital paper built on E-Ink technology. By reviewing literature on </p><p>navigation, and by handling input from both newspaper designers and potential e-newspaper </p><p>adopters, we have suggested a number of navigational aids for the future e-newspaper. Our </p><p>suggestions have been tested through a prototype using a modification of the Cooperative </p><p>Usability Testing model (CUT) on a total of fifteen potential users. The results show that most </p><p>users prefer a linear organization of the e-newspaper. To render a sense of depth in this flat </p><p>medium, page-numbering is essential. Several different interaction possibilities will also be </p><p>needed to ease the navigation and our suggestions include a blend of page-turning and hyper </p><p>linking, combined with an index to give the user a better overview of the content.</p>

Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.