Perception Intelligence Integrated Vehicle-to-Vehicle Optical Camera Communication.

Ubiquitous usage of cameras and LEDs in modern road and aerial vehicles open up endless opportunities for novel applications in intelligent machine navigation, communication, and networking. To this end, in this thesis work, we hypothesize the benefit of dual-mode usage of vehicular built-in cameras through novel machine perception capabilities combined with optical camera communication (OCC). Current key conception of understanding a line-of-sight (LOS) scenery is from the aspect of object, event, and road situation detection. However, the idea of blending the non-line-of-sight (NLOS) information with the LOS information to achieve a see-through vision virtually is new. This improves the assistive driving performance by enabling a machine to see beyond occlusion. Another aspect of OCC in the vehicular setup is to understand the nature of mobility and its impact on the optical communication channel quality. The research questions gathered from both the car-car mobility modelling, and evaluating a working setup of OCC communication channel can also be inherited to aerial vehicular situations like drone-drone OCC. The aim of this thesis is to answer the research questions along these new application domains, particularly, (i) how to enable a virtual see-through perception in the car assisting system that alerts the human driver about the visible and invisible critical driving events to help drive more safely, (ii) how transmitter-receiver cars behaves while in the mobility and the overall channel performance of OCC in motion modality, (iii) how to help rescue lost Unmanned Aerial Vehicles (UAVs) through coordinated localization with fusion of OCC and WiFi, (iv) how to model and simulate an in-field drone swarm operation experience to design and validate UAV coordinated localization for group of positioning distressed drones. In this regard, in this thesis, we present the end-to-end system design, proposed novel algorithms to solve the challenges in applying such a system, and evaluation results through experimentation and/or simulation

Humanoid Robot Soccer Locomotion and Kick Dynamics: Open Loop Walking, Kicking and Morphing into Special Motions on the Nao Robot

Striker speed and accuracy in the RoboCup (SPL) international robot soccer league is becoming increasingly important as the level of play rises. Competition around the ball is now decided in a matter of seconds. Therefore, eliminating any wasted actions or motions is crucial when attempting to kick the ball. It is common to see a discontinuity between walking and kicking where a robot will return to an initial pose in preparation for the kick action. In this thesis we explore the removal of this behaviour by developing a transition gait that morphs the walk directly into the kick back swing pose. The solution presented here is targeted towards the use of the Aldebaran walk for the Nao robot. The solution we develop involves the design of a central pattern generator to allow for controlled steps with realtime accuracy, and a phase locked loop method to synchronise with the Aldebaran walk so that precise step length control can be activated when required. An open loop trajectory mapping approach is taken to the walk that is stabilized statically through the use of a phase varying joint holding torque technique. We also examine the basic princples of open loop walking, focussing on the commonly overlooked frontal plane motion. The act of kicking itself is explored both analytically and empirically, and solutions are provided that are versatile and powerful. Included as an appendix, the broader matter of striker behaviour (process of goal scoring) is reviewed and we present a velocity control algorithm that is very accurate and efficient in terms of speed of execution

On the Enhancement of the Localization of Autonomous Mobile Platforms

The focus of many industrial and research entities on achieving full robotic autonomy increased in the past few years. In order to achieve full robotic autonomy, a fundamental problem is the localization, which is the ability of a mobile platform to determine its position and orientation in the environment. In this thesis, several problems related to the localization of autonomous platforms are addressed, namely, visual odometry accuracy and robustness; uncertainty estimation in odometries; and accurate multi-sensor fusion-based localization. Beside localization, the control of mobile manipulators is also tackled in this thesis. First, a generic image processing pipeline is proposed which, when integrated with a feature-based Visual Odometry (VO), can enhance robustness, accuracy and reduce the accumulation of errors (drift) in the pose estimation. Afterwards, since odometries (e.g. wheel odometry, LiDAR odometry, or VO) suffer from drift errors due to integration, and because such errors need to be quantified in order to achieve accurate localization through multi-sensor fusion schemes (e.g. extended or unscented kalman filters). A covariance estimation algorithm is proposed, which estimates the uncertainty of odometry measurements using another sensor which does not rely on integration. Furthermore, optimization-based multi-sensor fusion techniques are known to achieve better localization results compared to filtering techniques, but with higher computational cost. Consequently, an efficient and generic multi-sensor fusion scheme, based on Moving Horizon Estimation (MHE), is developed. The proposed multi-sensor fusion scheme: is capable of operating with any number of sensors; and considers different sensors measurements rates, missing measurements, and outliers. Moreover, the proposed multi-sensor scheme is based on a multi-threading architecture, in order to reduce its computational cost, making it more feasible for practical applications. Finally, the main purpose of achieving accurate localization is navigation. Hence, the last part of this thesis focuses on developing a stabilization controller of a 10-DOF mobile manipulator based on Model Predictive Control (MPC). All of the aforementioned works are validated using numerical simulations; real data from: EU Long-term Dataset, KITTI Dataset, TUM Dataset; and/or experimental sequences using an omni-directional mobile robot. The results show the efficacy and importance of each part of the proposed work

Multi-Object Tracking System based on LiDAR and RADAR for Intelligent Vehicles applications

El presente Trabajo Fin de Grado tiene como objetivo el desarrollo de un Sistema de Detección y Multi-Object Tracking 3D basado en la fusión sensorial de LiDAR y RADAR para aplicaciones de conducción autónoma basándose en algoritmos tradicionales de Machine Learning. La implementación realizada está basada en Python, ROS y cumple requerimientos de tiempo real. En la etapa de detección de objetos se utiliza el algoritmo de segmentación del plano RANSAC, para una posterior extracción de Bounding Boxes mediante DBSCAN. Una Late Sensor Fusion mediante Intersection over Union 3D y un sistema de tracking BEV-SORT completan la arquitectura propuesta.This Final Degree Project aims to develop a 3D Multi-Object Tracking and Detection System based on the Sensor Fusion of LiDAR and RADAR for autonomous driving applications based on traditional Machine Learning algorithms. The implementation is based on Python, ROS and complies with real-time requirements. In the Object Detection stage, the RANSAC plane segmentation algorithm is used, for a subsequent extraction of Bounding Boxes using DBSCAN. A Late Sensor Fusion using Intersection over Union 3D and a BEV-SORT tracking system complete the proposed architecture.Grado en Ingeniería en Electrónica y Automática Industria

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Aeronautical Engineering: A continuing bibliography with indexes, supplement 110

This bibliography lists 504 reports, articles, and other documents introduced into the NASA scientific and technical information system in May 1979

Cumulative index to NASA Tech Briefs, 1963-1967

Cumulative index to NASA survey on technology utilization of aerospace research outpu

Technology and Management for Sustainable Buildings and Infrastructures

A total of 30 articles have been published in this special issue, and it consists of 27 research papers, 2 technical notes, and 1 review paper. A total of 104 authors from 9 countries including Korea, Spain, Taiwan, USA, Finland, China, Slovenia, the Netherlands, and Germany participated in writing and submitting very excellent papers that were finally published after the review process had been conducted according to very strict standards. Among the published papers, 13 papers directly addressed words such as sustainable, life cycle assessment (LCA) and CO2, and 17 papers indirectly dealt with energy and CO2 reduction effects. Among the published papers, there are 6 papers dealing with construction technology, but a majority, 24 papers deal with management techniques. The authors of the published papers used various analysis techniques to obtain the suggested solutions for each topic. Listed by key techniques, various techniques such as Analytic Hierarchy Process (AHP), the Taguchi method, machine learning including Artificial Neural Networks (ANNs), Life Cycle Assessment (LCA), regression analysis, Strength–Weakness–Opportunity–Threat (SWOT), system dynamics, simulation and modeling, Building Information Model (BIM) with schedule, and graph and data analysis after experiments and observations are identified

Reckless motion estimation from omnidirectional image and inertial measurements

Two approaches to improving the accuracy of camera motion estimation from image sequences are the use of omnidirectional cameras, which combine a conventional camera with a convex mirror that magnifies the field of view, and the use of both image and inertial measurements, which are highly complementary. In this paper, we describe optimal batch algorithms for estimating motion and scene structure from either conventional or omnidirectional images, with or without inertial data. We also present a method for motion estimation from inertial data and the tangential components of image projections. Tangential components are identical across a wide range of conventional and omnidirectional projection models, so the resulting method does not require any accurate projection model. Because this method discards half of the projection data (i.e., the radial components) and can operate with a projection model that may grossly mismodel the actual camera behavior, we call the method ?eckless?motion estimation, but we show that the camera positions and scene structure estimated using this method can be quite accurate.</p