Safe navigation for vehicles

La navigation par satellite prend un virage très important ces dernières années, d'une part par l'arrivée imminente du système Européen GALILEO qui viendra compléter le GPS Américain, mais aussi et surtout par le succès grand public qu'il connaît aujourd'hui. Ce succès est dû en partie aux avancées technologiques au niveau récepteur, qui, tout en autorisant une miniaturisation de plus en plus avancée, en permettent une utilisation dans des environnements de plus en plus difficiles. L'objectif aujourd'hui est de préparer l'utilisation de ce genre de signal dans une optique bas coût dans un milieu urbain automobile pour des applications critiques d'un point de vue sécurité (ce que ne permet pas les techniques d'hybridation classiques). L'amélioration des technologies (réduction de taille des capteurs type MEMS ou Gyroscope) ne peut, à elle seule, atteindre l'objectif d'obtenir une position dont nous pouvons être sûrs si nous utilisons les algorithmes classiques de localisation et d'hybridation. En effet ces techniques permettent d'avoir une position sans cependant permettre d'en quantifier le niveau de confiance. La faisabilité de ces applications repose d'une part sur une recherche approfondie d'axes d'amélioration des algorithmes de localisation, mais aussi et conjointement, sur la possibilité, via les capteurs externes de maintenir un niveau de confiance élevé et quantifié dans la position même en absence de signal satellitaire. ABSTRACT : Satellite navigation has acquired an increased importance during these last years, on the one hand due to the imminent appearance of the European GALILEO system that will complement the American GPS, and on the other hand due to the great success it has encountered in the commercial civil market. An important part of this success is based on the technological development at the receiver level that has rendered satellite navigation possible even in difficult environments. Today's objective is to prepare the utilisation of this kind of signals for land vehicle applications demanding high precision positioning. One of the main challenges within this research domain, which cannot be addressed by classical coupling techniques, is related to the system capability to provide reliable position estimations. The enhancement in dead-reckoning technologies (i.e. size reduction of MEMS-based sensors or gyroscopes) cannot all by itself reach the necessary confidence levels if exploited with classical localization and integration algorithms. Indeed, these techniques provide a position estimation whose reliability or confidence level it is very difficult to quantify. The feasibility of these applications relies not only on an extensive research to enhance the navigation algorithm performances in harsh scenarios, but also and in parallel, on the possibility to maintain, thanks to the presence of additional sensors, a high confidence level on the position estimation even in the absence of satellite navigation signals

Safe navigation for vehicles

Satellite navigation has acquired an increased importance during these last years, on the one hand due to the imminent appearance of the European GALILEO system that will complement the American GPS, and on the other hand due to the great success it has encountered in the commercial civil market. An important part of this success is based on the technological development at the receiver level that has rendered satellite navigation possible even in difficult environments. Today's objective is to prepare the utilisation of this kind of signals for land vehicle applications demanding high precision positioning. One of the main challenges within this research domain, which cannot be addressed by classical coupling techniques, is related to the system capability to provide reliable position estimations. The enhancement in dead-reckoning technologies (i.e. size reduction of MEMS-based sensors or gyroscopes) cannot all by itself reach the necessary confidence levels if exploited with classical localization and integration algorithms. Indeed, these techniques provide a position estimation whose reliability or confidence level it is very difficult to quantify. The feasibility of these applications relies not only on an extensive research to enhance the navigation algorithm performances in harsh scenarios, but also and in parallel, on the possibility to maintain, thanks to the presence of additional sensors, a high confidence level on the position estimation even in the absence of satellite navigation signals

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion

Human-robot spatial interaction using probabilistic qualitative representations

Current human-aware navigation approaches use a predominantly metric representation of the interaction which makes them susceptible to changes in the environment. In order to accomplish reliable navigation in ever-changing human populated environments, the presented work aims to abstract from the underlying metric representation by using Qualitative Spatial Relations (QSR), namely the Qualitative Trajectory Calculus (QTC), for Human-Robot Spatial Interaction (HRSI). So far, this form of representing HRSI has been used to analyse different types of interactions online. This work extends this representation to be able to classify the interaction type online using incrementally updated QTC state chains, create a belief about the state of the world, and transform this high-level descriptor into low-level movement commands. By using QSRs the system becomes invariant to change in the environment, which is essential for any form of long-term deployment of a robot, but most importantly also allows the transfer of knowledge between similar encounters in different environments to facilitate interaction learning. To create a robust qualitative representation of the interaction, the essence of the movement of the human in relation to the robot and vice-versa is encoded in two new variants of QTC especially designed for HRSI and evaluated in several user studies. To enable interaction learning and facilitate reasoning, they are employed in a probabilistic framework using Hidden Markov Models (HMMs) for online classiffication and evaluation of their appropriateness for the task of human-aware navigation. In order to create a system for an autonomous robot, a perception pipeline for the detection and tracking of humans in the vicinity of the robot is described which serves as an enabling technology to create incrementally updated QTC state chains in real-time using the robot's sensors. Using this framework, the abstraction and generalisability of the QTC based framework is tested by using data from a different study for the classiffication of automatically generated state chains which shows the benefits of using such a highlevel description language. The detriment of using qualitative states to encode interaction is the severe loss of information that would be necessary to generate behaviour from it. To overcome this issue, so-called Velocity Costmaps are introduced which restrict the sampling space of a reactive local planner to only allow the generation of trajectories that correspond to the desired QTC state. This results in a exible and agile behaviour I generation that is able to produce inherently safe paths. In order to classify the current interaction type online and predict the current state for action selection, the HMMs are evolved into a particle filter especially designed to work with QSRs of any kind. This online belief generation is the basis for a exible action selection process that is based on data acquired using Learning from Demonstration (LfD) to encode human judgement into the used model. Thereby, the generated behaviour is not only sociable but also legible and ensures a high experienced comfort as shown in the experiments conducted. LfD itself is a rather underused approach when it comes to human-aware navigation but is facilitated by the qualitative model and allows exploitation of expert knowledge for model generation. Hence, the presented work bridges the gap between the speed and exibility of a sampling based reactive approach by using the particle filter and fast action selection, and the legibility of deliberative planners by using high-level information based on expert knowledge about the unfolding of an interaction

Dynamic state estimation for mobile robots

The scientific goal of this thesis is to tackle different approaches for effective state estimation and modelling of relevant problems in the context of mobile robots. The starting point of this dissertation is the concept of probabilistic robotics, an emerging paradigm that combines state-of-the-art methods with the classic probabilistic theory, developing stochastic frameworks for understanding the uncertain nature of the interaction between a robot and its environment. This allows introducing relevant concepts which are the foundation of the localisation system implemented on the main experimental platform used on this dissertation. An accurate estimation of the position of a robot with respect to a fixed frame is fundamental for building navigation systems that can work in dynamic unstructured environments. This development also allows introducing additional contributions related with global localisation, dynamic obstacle avoidance, path planning and position tracking problems. Kinematics on generalised manipulators are characterised for dealing with complex nonlinear systems. Nonlinear formulations are needed to properly model these systems, which are not always suitable for real-time realisation, lacking analytic formulations in most cases. In this context, this thesis tackles the serial-parallel dual kinematic problem with a novel approach, demonstrating state-of-the-art accuracy and real-time performance. With a spatial decomposition method, the forward kinematics problem on parallel robots and the inverse kinematics problem on serial manipulators is solved modelling the nonlinear behaviour of the pose space using Support Vector Machines. The results are validated on different topologies with the analytic solution for such manipulators, which demonstrates the applicability of the proposed method. Modelling and control of complex dynamical systems is another relevant field with applications on mobile robots. Nonlinear techniques are usually applied to tackle problems like feature or object tracking. However, some nonlinear integer techniques applied for tasks like position tracking in mobile robots with complex dynamics have limited success when modelling such systems. Fractional calculus has demonstrated to be suitable to model complex processes like viscoelasticity or super diffusion. These tools, that take advantage of the generalization of the derivative and integral operators to a fractional order, have been applied to model and control different topics related with robotics in recent years with remarkable success. With the proposal of a fractional-order PI controller, a suitable controller design method is presented to solve the position tracking problem. This is applied to control the distance of a self-driving car with respect to an objective, which can also be applied to other tracking applications like following a navigation path. Furthermore, this thesis introduces a novel fractional-order hyperchaotic system, stabilised with a full-pseudo-state-feedback controller and a located feedback method. This theoretical contribution of a chaotic system is introduced hoping to be useful in this context. Chaos theory has recently started to be applied to study manipulators, biped robots and autonomous navigation, achieving new and promising results, highlighting the uncertain and chaotic nature which also has been found on robots. All together, this thesis is devoted to different problems related with dynamic state estimation for mobile robots, proposing specific contributions related with modelling and control of complex nonlinear systems. These findings are presented in the context of a self-driving electric car, Verdino, jointly developed in collaboration with the Robotics Group of Universidad de La Laguna (GRULL)

Human perception capabilities for socially intelligent domestic service robots

The daily living activities for an increasing number of frail elderly people represent a continuous struggle both for them as well as for their extended families. These people have difficulties coping at home alone but are still sufficiently fit not to need the round-the-clock care provided by a nursing home. Their struggle can be alleviated by the deployment of a mechanical helper in their home, i.e. a service robot that can execute a range of simple object manipulation tasks. Such a robotic application promises to extend the period of independent home living for elderly people, while providing them with a better quality of life. However, despite the recent technological advances in robotics, there are still some remaining challenges, mainly related to the human factors. Arguably, the lack of consistently dependable human detection, localisation, position and pose tracking information and insufficiently refined processing of sensor information makes the close range physical interaction between a robot and a human a high-risk task. The work described in this thesis addresses the deficiencies in the processing of the human information of today’s service robots. This is achieved through proposing a new paradigm for the robot’s situational awareness in regard to people as well as a collection of methods and techniques, operating at the lower levels of the paradigm, i.e. perception of new human information. The collection includes methods for obtaining and processing of information about the presence, location and body pose of the people. In addition to the availability of reliable human perception information, the integration between the separate levels of paradigm is considered to be a critically important factor for achieving the human-aware control of the robot. Improving the cognition, judgment and decision making action links between the paradigm’s layers leads to enhanced capability of the robot to engage in a natural and more meaningful interaction with people and, therefore, to a more enjoyable user experience. Therefore, the proposed paradigm and methodology are envisioned to contribute to making the prolonged assisted living of elderly people at home a more feasible and realistic task. In particular, this thesis proposes a set of methods for human presence detection, localisation and body pose tracking that are operating on the perception level of the paradigm. Also, the problem of having only limited visibility of a person from the on-board sensors of the robot is addressed by the proposed classifier fusion method that combines information from several types of sensors. A method for improved real-time human body pose tracking is also investigated. Additionally, a method for estimation of the multiple human tracks from noisy detections, as well as analysis of the computed human tracks for cognition about the social interactions within the social group, operating at the comprehension level of the robot’s situational awareness paradigm, is proposed. Finally, at the human-aware planning layer, a method that utilises the human related information, generated by the perception and comprehension layers to compute a minimally intrusive navigation path to a target person within a human group, is proposed. This method demonstrates how the improved human perception capabilities of the robot, through its judgement activity, ii ABSTRACT can be utilised by the highest level of the paradigm, i.e. the decision making layer, to achieve user friendly human-robot interactions. Overall, the research presented in this work, drawing on recent innovation in statistical learning, data fusion and optimisation methods, improves the overall situational awareness of the robot in regard to people with the main focus placed on human sensing capabilities of service robots. The improved overall situational awareness of the robot regarding people, as defined by the proposed paradigm, enables more meaningful human-robot interactions

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Location-Based Sensor Fusion for UAS Urban Navigation.

For unmanned aircraft systems (UAS) to effectively conduct missions in urban environments, a multi-sensor navigation scheme must be developed that can operate in areas with degraded Global Positioning System (GPS) signals. This thesis proposes a sensor fusion plug and play capability for UAS navigation in urban environments to test combinations of sensors. Measurements are fused using both the Extended Kalman Filter (EKF) and Ensemble Kalman Filter (EnKF), a type of Particle Filter. A Long Term Evolution (LTE) transceiver and computer vision sensor each augment the traditional GPS receiver, inertial sensors, and air data system. Availability and accuracy information for each sensor is extracted from the literature. LTE positioning is motivated by a perpetually expanding network that can provide persistent measurements in the urban environment. A location-based logic model is proposed to predict sensor availability and accuracy for a given type of urban environment based on a map database as well as real-time sensor inputs and filter outputs. The simulation is executed in MATLAB where the vehicle dynamics, environment, sensors, and filters are user-customizable. Results indicate that UAS horizontal position accuracy is most dependent on availability of high sampling rate position measurements along with GPS measurement availability. Since the simulation is able to accept LTE sensor specifications, it will be able to show how the UAS position accuracy can be improved in the future with this persistent measurement, even though the accuracy is not improved using current LTE state-of-the-art. In the unmatched true propagation and filter dynamics model scenario, filter tuning proves to be difficult as GPS availability varies from urban canyon to urban canyon. The main contribution of this thesis is the generation of accuracy data for different sensor suites in both a homogeneous urban environment (solid walls) using matched dynamics models and a heterogeneous urban environment layout using unmatched models that necessitate filter tuning. Future work should explore the use of downward facing VISION sensors and LiDAR, integrate real-time map information into sensor availability and measurement weighting decisions, including the use of LTE for approximate localization, and more finely represent expected measurement accuracies in the GPS and LTE networks.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110361/1/jrufa_1.pd