Simultaneous image color correction and enhancement using particle swarm optimization

Color images captured under various environments are often not ready to deliver the desired quality due to adverse effects caused by uncontrollable illumination settings. In particular, when the illuminate color is not known a priori, the colors of the objects may not be faithfully reproduced and thus impose difficulties in subsequent image processing operations. Color correction thus becomes a very important pre-processing procedure where the goal is to produce an image as if it is captured under uniform chromatic illumination. On the other hand, conventional color correction algorithms using linear gain adjustments focus only on color manipulations and may not convey the maximum information contained in the image. This challenge can be posed as a multi-objective optimization problem that simultaneously corrects the undesirable effect of illumination color cast while recovering the information conveyed from the scene. A variation of the particle swarm optimization algorithm is further developed in the multi-objective optimization perspective that results in a solution achieving a desirable color balance and an adequate delivery of information. Experiments are conducted using a collection of color images of natural objects that were captured under different lighting conditions. Results have shown that the proposed method is capable of delivering images with higher quality. © 2013 Elsevier Ltd. All rights reserved

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Study of compliant mechanisms and flexible hinges in topology optimization

This thesis presents a comprehensive study on the application of compliant mechanisms and flexible hinges in topology optimization. Compliant mechanisms are a promising approach for achieving desired functionalities and structural flexibility in engineering designs. By exploiting the inherent elasticity of materials, compliant mechanisms offer advantages such as reduced complexity, improved reliability, and enhanced performance. Topology optimization, conversely, allows obtaining compliant mechanisms with reduced weight through the creation of holes, thus achieving an optimized design. In this work, we explore the integration of compliant mechanisms and flexible hinges within the framework of topology optimization, aiming to propose a method of improvement for the design efficiency and performance of structures in the aerospace field. The thesis begins with a thorough literature review of compliant mechanisms and their role in current aerospace applications. Various design principles and analysis techniques are examined to establish a solid foundation for the subsequent chapters. The study then focuses on the implementation of mathematical models and computational algorithms to incorporate compliant mechanisms and flexible hinges into the topology optimization process. To validate the proposed approach, a series of numerical experiments are conducted. Various case studies are considered, including a gripping and inverter mechanisms. The results demonstrate the effectiveness of compliant mechanisms and flexible hinges in enhancing the performance of optimized structures. The compliant mechanisms exhibit improved flexibility, adaptability, and energy absorption capabilities enabling smooth and controlled motion. Overall, this thesis significantly contributes to the understanding and implementation of compliant mechanisms and their integration with topology optimization techniques. The study not only showcases their potential for creating innovative and efficient designs across various engineering disciplines but also emphasizes their particular relevance in the aerospace field. By exploring the application of compliant mechanisms and topology optimization in aerospace engineering, it has been seen that this cutting-edge technology is opened up for new avenues for further research and development

A study of word association aids in information retrieval

Issued as Final project reports [nos. 1-2], Project no. G-36-65

Optimal Collision Avoidance Trajectories for Unmanned/Remotely Piloted Aircraft

The post-911 environment has punctuated the force-multiplying capabilities that Remotely Piloted Aircraft (RPA) provides combatant commanders at all echelons on the battlefield. Not only have unmanned aircraft systems made near-revolutionary impacts on the battlefield, their utility and proliferation in law enforcement, homeland security, humanitarian operations, and commercial applications have likewise increased at a rapid rate. As such, under the Federal Aviation Administration (FAA) Modernization and Reform Act of 2012, the United States Congress tasked the FAA to provide for the safe integration of civil unmanned aircraft systems into the national airspace system (NAS) as soon as practicable, but not later than September 30, 2015. However, a necessary entrance criterion to operate RPAs in the NAS is the ability to Sense and Avoid (SAA) both cooperative and noncooperative air traffic to attain a target level of safety as a traditional manned aircraft platform. The goal of this research effort is twofold: First, develop techniques for calculating optimal avoidance trajectories, and second, develop techniques for estimating an intruder aircraft\u27s trajectory in a stochastic environment. This dissertation describes the optimal control problem associated with SAA and uses a direct orthogonal collocation method to solve this problem and then analyzes these results for different collision avoidance scenarios

Safe Navigation of Quadruped Robots Using Density Functions

Safe navigation of mission-critical systems is of utmost importance in many modern autonomous applications. Over the past decades, the approach to the problem has consisted of using probabilistic methods, such as sample-based planners, to generate feasible, safe solutions to the navigation problem. However, these methods use iterative safety checks to guarantee the safety of the system, which can become quite complex. The navigation problem can also be solved in feedback form using potential field methods. Navigation function, a class of potential field methods, is an analytical control design to give almost everywhere convergence properties, but under certain topological constraints and mapping onto a sphere world. Alternatively, the navigation problem can be formulated in the dual space of density. Recent works have shown the use of linear operator theory on density to convexly approach the navigation problem. Inspired by those works, this work uses the physical-based interpretation of occupation through density to synthesize a safe controller for the navigation problem. Moreso, by using this occupation-based interpretation of density, we design a feedback density-based controller to solve the almost everywhere navigation problem. Furthermore, due to the recent popularity of legged locomotion for the navigation problem, we integrate this analytical feedback density-based controller into the quadruped navigation problem. By devising a density-based navigation architecture, we show in simulation and hardware the results of the density-based navigation

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen