Vision Based Vehicle Localization for Infrastructure Enabled Autonomy

Primary objective of this research is to devise techniques to localize an autonomous vehicle in an Infrastructure Enabled Autonomy (IEA) setup. IEA is a new paradigm in autonomous vehicles research that aims at distributed intelligence architecture by transferring the core functionalities of sensing and localization to infrastructure. This paradigm is also promising in designing large scalable systems that enable autonomous car platooning on highways. A reliable camera calibration technique for such an experimental setup is discussed, followed by the technique for 2D image to 3D world coordinate transformation. In this research, information is received from: (1) on-board vehicle sensors like GPS and IMU, (2) localized car position data derived from deep learning on the real-time camera feeds and (3) lane detection data from infrastructure cameras. This data is fused together utilizing an Extended Kalman Filter (EKF) to obtain reliable position estimates of the vehicle at 50 Hz. This position information is then used to control the vehicle with an objective of following a prescribed path. Extensive simulation and experimental results are also presented to corroborate the performance of the proposed approach

Vision Based Vehicle Localization for Infrastructure Enabled Autonomy

Primary objective of this research is to devise techniques to localize an autonomous vehicle in an Infrastructure Enabled Autonomy (IEA) setup. IEA is a new paradigm in autonomous vehicles research that aims at distributed intelligence architecture by transferring the core functionalities of sensing and localization to infrastructure. This paradigm is also promising in designing large scalable systems that enable autonomous car platooning on highways. A reliable camera calibration technique for such an experimental setup is discussed, followed by the technique for 2D image to 3D world coordinate transformation. In this research, information is received from: (1) on-board vehicle sensors like GPS and IMU, (2) localized car position data derived from deep learning on the real-time camera feeds and (3) lane detection data from infrastructure cameras. This data is fused together utilizing an Extended Kalman Filter (EKF) to obtain reliable position estimates of the vehicle at 50 Hz. This position information is then used to control the vehicle with an objective of following a prescribed path. Extensive simulation and experimental results are also presented to corroborate the performance of the proposed approach

Subject-Independent Frameworks for Robotic Devices: Applying Robot Learning to EMG Signals

The capability of having human and robots cooperating together has increased the interest in the control of robotic devices by means of physiological human signals. In order to achieve this goal it is crucial to be able to catch the human intention of movement and to translate it in a coherent robot action. Up to now, the classical approach when considering physiological signals, and in particular EMG signals, is to focus on the specific subject performing the task since the great complexity of these signals. This thesis aims to expand the state of the art by proposing a general subject-independent framework, able to extract the common constraints of human movement by looking at several demonstration by many different subjects. The variability introduced in the system by multiple demonstrations from many different subjects allows the construction of a robust model of human movement, able to face small variations and signal deterioration. Furthermore, the obtained framework could be used by any subject with no need for long training sessions. The signals undergo to an accurate preprocessing phase, in order to remove noise and artefacts. Following this procedure, we are able to extract significant information to be used in online processes. The human movement can be estimated by using well-established statistical methods in Robot Programming by Demonstration applications, in particular the input can be modelled by using a Gaussian Mixture Model (GMM). The performed movement can be continuously estimated with a Gaussian Mixture Regression (GMR) technique, or it can be identified among a set of possible movements with a Gaussian Mixture Classification (GMC) approach. We improved the results by incorporating some previous information in the model, in order to enriching the knowledge of the system. In particular we considered the hierarchical information provided by a quantitative taxonomy of hand grasps. Thus, we developed the first quantitative taxonomy of hand grasps considering both muscular and kinematic information from 40 subjects. The results proved the feasibility of a subject-independent framework, even by considering physiological signals, like EMG, from a wide number of participants. The proposed solution has been used in two different kinds of applications: (I) for the control of prosthesis devices, and (II) in an Industry 4.0 facility, in order to allow human and robot to work alongside or to cooperate. Indeed, a crucial aspect for making human and robots working together is their mutual knowledge and anticipation of other’s task, and physiological signals are capable to provide a signal even before the movement is started. In this thesis we proposed also an application of Robot Programming by Demonstration in a real industrial facility, in order to optimize the production of electric motor coils. The task was part of the European Robotic Challenge (EuRoC), and the goal was divided in phases of increasing complexity. This solution exploits Machine Learning algorithms, like GMM, and the robustness was assured by considering demonstration of the task from many subjects. We have been able to apply an advanced research topic to a real factory, achieving promising results

Epistemologien des Umgebens: Zur Geschichte, Ökologie und Biopolitik künstlicher environments

Der Aufstieg des Begriffs "Environment" zur Beschreibung der Gegenwart markiert den Einfluss, den das Nachdenken über Umgebungsrelationen und die Möglichkeit der technischen Gestaltung künstlicher Umgebungen seit Mitte des 19. Jahrhunderts gewonnen haben. In geschlossenen artifiziellen Welten wie Raumstationen oder künstlichen Ökosystemen wird die Verschränkung des "Environments" mit den umgebenen Organismen zum Gegenstand einer Biopolitik, die heute in autonomen Technologien der Umgebungskontrolle neue Räume erschließt. Der Autor verfolgt diese Transformation ökologischen Umgebungswissens mit dem Ziel, gegenwärtige Technologien besser zu verstehen, den Begriff unselbstverständlich zu machen und die biopolitische Dimension jeder Ökologie herauszuarbeiten