299 research outputs found

    A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

    Get PDF
    Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

    Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems

    Get PDF
    Recent successes combine reinforcement learning algorithms and deep neural networks, despite reinforcement learning not being widely applied to robotics and real world scenarios. This can be attributed to the fact that current state-of-the-art, end-to-end reinforcement learning approaches still require thousands or millions of data samples to converge to a satisfactory policy and are subject to catastrophic failures during training. Conversely, in real world scenarios and after just a few data samples, humans are able to either provide demonstrations of the task, intervene to prevent catastrophic actions, or simply evaluate if the policy is performing correctly. This research investigates how to integrate these human interaction modalities to the reinforcement learning loop, increasing sample efficiency and enabling real-time reinforcement learning in robotics and real world scenarios. This novel theoretical foundation is called Cycle-of-Learning, a reference to how different human interaction modalities, namely, task demonstration, intervention, and evaluation, are cycled and combined to reinforcement learning algorithms. Results presented in this work show that the reward signal that is learned based upon human interaction accelerates the rate of learning of reinforcement learning algorithms and that learning from a combination of human demonstrations and interventions is faster and more sample efficient when compared to traditional supervised learning algorithms. Finally, Cycle-of-Learning develops an effective transition between policies learned using human demonstrations and interventions to reinforcement learning. The theoretical foundation developed by this research opens new research paths to human-agent teaming scenarios where autonomous agents are able to learn from human teammates and adapt to mission performance metrics in real-time and in real world scenarios.Comment: PhD thesis, Aerospace Engineering, Texas A&M (2020). For more information, see https://vggoecks.com

    Activity Report 2022

    Get PDF

    Solving multiobjective constrained trajectory optimization problem by an extended evolutionary algorithm

    Get PDF
    Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems

    Aerial Vehicles

    Get PDF
    This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

    Cyber-Human Systems, Space Technologies, and Threats

    Get PDF
    CYBER-HUMAN SYSTEMS, SPACE TECHNOLOGIES, AND THREATS is our eighth textbook in a series covering the world of UASs / CUAS/ UUVs / SPACE. Other textbooks in our series are Space Systems Emerging Technologies and Operations; Drone Delivery of CBNRECy – DEW Weapons: Emerging Threats of Mini-Weapons of Mass Destruction and Disruption (WMDD); Disruptive Technologies with applications in Airline, Marine, Defense Industries; Unmanned Vehicle Systems & Operations On Air, Sea, Land; Counter Unmanned Aircraft Systems Technologies and Operations; Unmanned Aircraft Systems in the Cyber Domain: Protecting USA’s Advanced Air Assets, 2nd edition; and Unmanned Aircraft Systems (UAS) in the Cyber Domain Protecting USA’s Advanced Air Assets, 1st edition. Our previous seven titles have received considerable global recognition in the field. (Nichols & Carter, 2022) (Nichols, et al., 2021) (Nichols R. K., et al., 2020) (Nichols R. , et al., 2020) (Nichols R. , et al., 2019) (Nichols R. K., 2018) (Nichols R. K., et al., 2022)https://newprairiepress.org/ebooks/1052/thumbnail.jp

    Autonomous Weapons and Human Responsibilities

    Get PDF
    Although remote-controlled robots flying over the Middle East and Central Asia now dominate reports on new military technologies, robots that are capable of detecting, identifying, and killing enemies on their own are quietly but steadily movingfrom the theoretical to the practical. The enormous difficulty in assigning responsibilities to humans and states for the actions ofthese machines grows with their increasing autonomy. These developments implicate serious legal, ethical, and societal concerns. This Article focuses on the accountability of states and underlying human responsibilities for autonomous weapons under International Humanitarian Law or the Law of Armed Conflict. After reviewing the evolution of autonomous weapon systems and diminishing human involvement in these systems along a continuum of autonomy, this Article argues that the elusive search for individual culpability for the actions of autonomous weapons foreshadows fundamental problems in assigning responsibility to states for the actions of these machines. It further argues that the central legal requirement relevant to determining accountability (especially for violation of the most important international legal obligations protecting the civilian population in armed conflicts) is human judgment. Access to effective human judgment already appears to be emerging as the deciding factor in establishing practical restrictions and framing legal concerns with respect to the deployment of the most advanced autonomous weapons

    Operationalized Intent for Improving Coordination in Human-Agent Teams

    Get PDF
    With the increasing capabilities of artificial intelligent agents (AIAs) integrated into multi-agent systems, future concepts include human-agent teams (HATs) in which the members perform fluidly as a coordinated team. Research on coordination mechanisms in HATs is largely focused on AIAs providing information to humans to coordinate better (i.e. coordination from the AIA to the human). We focus on the compliment where AIAs can understand the operator to better synchronize with the operator (i.e. from the human to the AIA). This research focuses specifically on AIA estimation of operator intent. We established the Operationalized Intent framework which captures intent in a manner relevant to operators and AIAs. The core of operationalized intent is a quality goal hierarchy and an execution constraint list. Designing a quality goal hierarchy entails understanding the domain, the operators, and the AIAs. By extending established cognitive systems engineering analyses we developed a method to define the quality goals and capture the situations that influence their prioritization. Through a synthesis of mental model evaluation techniques, we defined and executed a process for designing human studies of intent. This human-in-the-loop study produced a corpus of data which was demonstrated the feasibility of estimating operationalized intent

    From gamestorming to mobile learning : a conceptual framework and a gaming proposition to explore the design of flourishing business models

    Get PDF
    Cette démarche de thèse débute par la mise au point d'un cadre conceptuel à propos de la durabilité ('sustainability') et du MA (modèle d'affaires), pour cadrer une recherche sur la définition et la conception de MA durable. Grâce notamment à Ehrenfeld (2005), le MAF (modèle d'affaires pour un avenir florissant ou 'flourishing future') est défini. La question est maintenant de savoir comment introduire les gestionnaires à la théorie et la pratique du MAF? Quelle est la nature de l'effort cognitif exigé? Et l'apprentissage peut-il être stimulé par le 'gamestorming' en proposant un espace d'apprentissage ouvert à la formation de nouveaux concepts. Le premier chapitre présente les origines du MA suite à l'affrontement dans les années 1980 entre la finance d'entreprise et la stratégie d'entreprise lors de la naissance du premier logiciel de tableur. Dès lors, le chapitre un propose d'envisager l'histoire du MA en trois périodes : d'abord le MA pour la valeur numérique, ensuite, le MA architectural et finalement, le MA durable. Mais les académiciens et les praticiens ne s'entendent pas sur la définition de MA durable. Il existe une opposition entre les approches faible et forte. Nous adoptons dans cette thèse la définition et l'engagement d'Ehrenfeld (2005) à un avenir florissant, définissant ainsi le MAF ou modèle d'affaires (pour un avenir) florissant. Le chapitre un montre que le MA pour la valeur numérique implique le calcul comme un mode cognitif, le MA architectural est plus associé à l'interprétation comme mode cognitif, tandis que MAF devrait être conçu grâce à la cognition située et à la macrocognition. Le chapitre deux oppose le MA développé sous une vision cognitive plus traditionnelle de computation-interprétation à la construction du MAF exigeant de nouvelles conditions préalables nécessaires à la cognition située et à la macrocognition. De cette façon, les acteurs conçoivent un MAF via leur interface sensorimotrice où le sens se dégage de multiples interactions avec la matérialité sociale et la matérialité physique du modèle. Aussi un MAF devient un objet public partagé, ouvert au développement de la compétence sociale dans une situation où les principes de macrocognition s'appliquent. Le chapitre trois fait le bilan d'une expérience d'enseignement / apprentissage avec une classe d'étudiants au MBA dans laquelle les étudiants devaient gérer dans le même cours, à la fois le canevas dédié au MA (CMA) et une modélisation organisationnelle plutôt abstraite reliée à la gestion des connaissances (Morabito et al., 1999). Cette expérience d'apprentissage est un cas de conception dense ('thick design') à l'intérieur d'une salle de classe inversée qui permet d'explorer l'idée suivante : si la matérialité sociale et physique fait partie du domaine de conception, les exigences de la cognition et la charge cognitive seront plus lourdes. Le chapitre se termine en associant durabilité faible avec un design mince ('thin') et la durabilité forte avec la conception dense ('thick'). Le chapitre quatre plonge plus profondément dans les questions de durabilité. Ce chapitre présente une expérience jeu avec Logim@s© qui s'est produite dans la division du développement durable d'une grande ville canadienne : les quatre joueurs étaient gestionnaires de développement durable ou professionnels dans le domaine. Le jeu est basé sur le livre de Steven Moore (2007) qui expose les scénarios, les modes logiques et les discours qui permettent à trois villes très différentes (Curitiba, Austin et Francfort) de déployer leur leadership en matière de durabilité. Un défi de conception dense est au cœur de l'expérience : comment un joueur peut-il utiliser l'approche CMA alors que des discours contradictoires risquent de le bloquer cognitivement? Les joueurs sont dans un mode logique inductif / déductif. Vont-ils passer en mode abductif? Le chapitre cinq examine comment le jeu Logim@s© pourrait devenir une plateforme ouverte de gamestorming, appelons-la SustAbd©. Ce chapitre comporte deux parties : la première partie est une réflexion sur le processus de conception de jeu pour justifier une approche plate-forme d'architecture composé du noyau SustAbd© et de sa périphérie, et une seconde partie, où cinq cas d'utilisation UML sont proposés. Le chapitre six s'appuie sur l'expérience du chercheur comme un tuteur humain dans les expériences d'enseignement inversé et de 'gamestorming.' Le but de ce chapitre est d'adopter la modélisation cognitive (MC) comme approche pour remplacer un tuteur humain par un robot 'situé.' Ce chapitre se poursuit avec des développements au sujet du caractère situé des robots. Ces idées permettent de concevoir SustAbdPLAY© conformément au caractère situé et aux conditions de macrocognition propres au design d'un MAF. La modélisation sociale avec iStar permet de clarifier la conception. Le chapitre sept termine la thèse. Il décrit les leçons apprises, les limites de l'étude ainsi que la suggestion de recherches futures. Une conclusion générale clôt le chapitre.\ud ______________________________________________________________________________ \ud MOTS-CLÉS DE L’AUTEUR : Business model, modèle d'affaires, soutenabilité, développement durable, cognition, matérialité, gamestorming, apprentissage mobile, recherche action, desig

    A Virtual Motion Camouflage Approach for Cooperative Trajectory Planning of Multiple UCAVs

    Get PDF
    This paper investigates cooperative trajectory planning of multiple unmanned combat aerial vehicles (multi-UCAV) in performing autonomous cooperative air-to-ground target attack missions. By integrating an approximate allowable attack region model, several constraint models, and a multicriterion objective function, the problem is formulated as a cooperative trajectory optimal control problem (CTOCP). Then, a virtual motion camouflage (VMC) for cooperative trajectory planning of multi-UCAV, combining with the differential flatness theory, Gauss pseudospectral method (GPM), and nonlinear programming, is designed to solve the CTOCP. In particular, the notion of the virtual time is introduced to the VMC problem formulation to handle the temporal cooperative constraints. The simulation experiments validate that the CTOCP can be effectively solved by the cooperative trajectory planning algorithm based on VMC which integrates the spatial and temporal constraints on the trajectory level, and the comparative experiments illustrate that VMC based algorithm is more efficient than GPM based direct collocation method in tackling the CTOCP
    • …
    corecore