4,897 research outputs found

    Intelligent Operation System for the Autonomous Vehicle Fleet

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    Modular vehicles are vehicles with interchangeable substantial components also known as modules. Fleet modularity provides extra operational flexibility through on-field actions, in terms of vehicle assembly, disassembly, and reconfiguration (ADR). The ease of assembly and disassembly of modular vehicles enables them to achieve real-time fleet reconfiguration, which is proven as beneficial in promoting fleet adaptability and in saving ownership costs. The objective of military fleet operation is to satisfy uncertain demands on time while providing vehicle maintenance. To quantify the benefits and burdens from modularity in military operation, a decision support system is required to yield autonomously operation strategies for comparing the (near) optimal fleet performance for different vehicle architectures under diverse scenarios. The problem is challenging because: 1) fleet operation strategies are numerous, especially when modularity is considered; 2) operation actions are time-delayed and time-varying; 3) vehicle damages and demands are highly uncertain; 4) available capacity for ADR actions and vehicle repair is constrained. Finally, to explore advanced tactics enabled by fleet modularity, the competition between human-like and adversarial forces is required, where each force is capable to autonomously perceive and analyze field information, learn enemy's behavior, forecast enemy's actions, and prepare an operation plan accordingly. Currently, methodologies developed specifically for fleet competition are only valid for single type of resources and simple operation rules, which are impossible to implement in modular fleet operation. This dissertation focuses on a new general methodology to yield decisions in operating a fleet of autonomous military vehicles/robots in both conventional and modular architectures. First, a stochastic state space model is created to represent the changes in fleet dynamics caused by operation actions. Then, a stochastic model predictive control is customized to manage the system dynamics, which is capable of real-time decision making. Including modularity increases the complexity of fleet operation problem, a novel intelligent agent based model is proposed to ensure the computational efficiency and also imitate the collaborative decisions making process of human-like commanders. Operation decisions are distributed to several agents with distinct responsibility. Agents are designed in a specific way to collaboratively make and adjust decisions through selectively sharing information, reasoning the causality between events, and learning the other's behavior, which are achieved by real-time optimization and artificial intelligence techniques. To evaluate the impacts from fleet modularity, three operation problems are formulated: (i) simplified logistic mission scenario: operate a fleet to guarantee the readiness of vehicles at battlefields considering the stochasticity in inventory stocks and mission requirements; (ii) tactical mission scenario: deliver resources to battlefields with stochastic requirements of vehicle repairs and maintenance; (iii) attacker-defender game: satisfy the mission requirements with minimized losses caused by uncertain assaults from an enemy. The model is also implemented for a civilian application, namely the real-time management of reconfigurable manufacturing systems (RMSs). As the number of RMS configurations increases exponentially with the size of the line and demand changes frequently, two challenges emerge: how to efficiently select the optimal configuration given limited resources, and how to allocate resources among lines. According to the ideas in modular fleet operation, a new mathematical approach is presented for distributing the stochastic demands and exchanging machines or modules among lines (which are groups of machines) as a bidding process, and for adaptively configuring these lines and machines for the resulting shared demand under a limited inventory of configurable components.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/1/lixingyu_2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147588/2/lixingyu_1.pd

    Advanced Robotic Radiative Process Control for Automotive Coatings

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    Design and Analysis of a Cylindrical Dielectric Resonator Antenna Array and Its Feed Network

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    There is an ever increasing need for smaller, lighter, more efficient antennas for commercial and military applications. One such antenna that meets these requirements is the dielectric resonator antenna (DRA). In recent years there has been an abundance of research on the utilization of the DRA as a radiating element. However, its practical application - especially pertaining to DRA arrays - is still considered to be at its infancy. The purpose of this work is to present a systematic process to be used in the design, simulation, optimization, fabrication, and testing of a cylindrical DRA array including its associated feed network. The DRA array development cycle begins with a single cylindrical radiating element. ComDRA parameters such as DRA radius, feed type, feed location, and element spacing are investigated. A DRA element in this research is optimized for bandwidth and gain for use at x-band (8-12 GHz). The antenna feed network, being an integral part of all antenna arrays, is also considered. The primary causes of impedance mismatch in the feed network are identified and techniques to improve performance are explored. An improvement in impedance bandwidth is gained through traditional transmission line matching methods. Ultimately, a 16 (4x4) element and 256 (16x16) element array is fabricated, tested, and compared to an existing commercial technology

    Technology for large space systems: A special bibliography with indexes (supplement 03)

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    A bibliography containing 217 abstracts addressing the technology for large space systems is presented. State of the art and advanced concepts concerning interactive analysis and design, structural concepts, control systems, electronics, advanced materials, assembly concepts, propulsion, solar power satellite systems, and flight experiments are represented

    Design of a Global Supply Chain for the Unexpected

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    Supply chains (SCs) play a crucial role in business operations and economies around the globe. They are in constant change and face challenges such as recurrent risks and disruption risks. The disruptive risks tend to cascade and propagate upstream and downstream of the disruption point. Due to the difficulty of calculating probabilities of disruptions, many decision makers prefer to underestimate disruptive risks. Losses of billions of dollars are accounted for each year due to the disruptive risks. These losses highlight the importance and need of having decision support systems and tools that can aid to design, model and analyze SCs that can cope with disruptions and their effects through all the stages. This research aims at developing new methods for designing and analyzing SCs that are prepared for unexpected events. It provides new insights into the methods to estimate the impact of possible disruptions during designing and planning stages. It further proposes complexity, robustness and resilience measures which facilitate the comparison between different SC designs in different scenarios. The significance of this research is to provide more stable production environments and develop the capability to prepare for unexpected events. Particular focus is given to natural disasters due to the magnitude and variety of impacts they could cause. Hence, a mathematical programming model that designs SCs and product architectures is proposed. The objective function is to minimize the disaster risk score of natural disasters (which depends on the geographical location of each SC entity and its associated “World Risk Index”). Also, a goal programming model is derived from the initial model. The goal programming model allows the inclusion of the decision-makers’ risk attitudes and costs to balance the decisions. The results obtained from the model showed that the SC and product architecture designs affect each other. Additionally, it was demonstrated that different risk-attitudes could lead to different SC designs. To achieve harmonious designs between SCs and products while remaining robust and controlling complexity, a novel methodology to assess structural SC complexity and robustness is presented using network analysis. This methodology includes the evaluation of different product architectures. Consequently, managers can choose the SC/product architecture that has a balanced level of complexity and robustness. It is worth noting that complexity and higher costs are needed to protect against disruptions. Moreover, the results demonstrated that the modular architecture is preferable as it has a balanced level of complexity and robustness. To analyze the dynamic behaviour of the SCs, a system dynamics framework is introduced to evaluate the impacts of disruptions in assembly SCs. Consequently, a pragmatic tool that provides organizational support is proposed. This framework enables the examination of full and partial disruptions and the incorporation of expediting orders after a disturbance. The SC performance indicators are the output of the proposed model. These indicators make the comparison between different scenarios easy. The usage of the framework and the findings can serve to define disruption policies, and assist in the decisions relating to the SC design. After running several scenarios, it was determined that the disruptions happening in the downstream levels have more impacts on the SC performance than the disruptions in the upstream levels. Hence, the disruption policies for the downstream levels should have higher priority. Moreover, it was demonstrated that expediting after disruptions could affect more the already damaged SC performance. Finally, to evaluate the SC performance and costs when facing disruptions, an index to assess SC resilience cost is provided. The metric considers the fulfilment rate in each period of each SC entity and its associated cost. This index allows comparison between different scenarios in the SC

    Aeronautical engineering: A continuing bibliography, supplement 122

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    This bibliography lists 303 reports, articles, and other documents introduced into the NASA scientific and technical information system in April 1980

    A Series-Elastic Robot for Back-Pain Rehabilitation

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    Robotics research has been broadly expanding into various fields during the past decades. It is widely spread and best known for solving many technical necessities in different fields. With the rise of the industrial revolution, it upgraded many factories to use industrial robots to prevent the human operator from dangerous and hazardous tasks. The rapid development of application fields and their complexity have inspired researchers in the robotics community to find innovative solutions to meet the new desired requirements of the field. Currently, the creation of new needs outside the traditional industrial robots are demanding robots to attend to the new market and to assist humans in meeting their daily social needs (i.e., agriculture, construction, cleaning.). The future integration of robots into other types of production processes, added new requirements that require more safety, flexibility, and intelligence in robots. Areas of robotics has evolved into various fields. This dissertation addresses robotics research in four different areas: rehabilitation robots, biologically inspired robots, optimization techniques, and neural network implementation. Although these four areas may seem different from each other, they share some research topics and applications

    Aeronautical Engineering: A special bibliography with indexes, supplement 72, July 1976

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    This bibliography lists 184 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976

    Periodic and Near-Periodic Structures

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