High-Performance Integrated Window and Façade Solutions for California

The researchers developed a new generation of high-performance façade systems and supporting design and management tools to support industry in meeting California’s greenhouse gas reduction targets, reduce energy consumption, and enable an adaptable response to minimize real-time demands on the electricity grid. The project resulted in five outcomes: (1) The research team developed an R-5, 1-inch thick, triplepane, insulating glass unit with a novel low-conductance aluminum frame. This technology can help significantly reduce residential cooling and heating loads, particularly during the evening. (2) The team developed a prototype of a windowintegrated local ventilation and energy recovery device that provides clean, dry fresh air through the façade with minimal energy requirements. (3) A daylight-redirecting louver system was prototyped to redirect sunlight 15–40 feet from the window. Simulations estimated that lighting energy use could be reduced by 35–54 percent without glare. (4) A control system incorporating physics-based equations and a mathematical solver was prototyped and field tested to demonstrate feasibility. Simulations estimated that total electricity costs could be reduced by 9-28 percent on sunny summer days through adaptive control of operable shading and daylighting components and the thermostat compared to state-of-the-art automatic façade controls in commercial building perimeter zones. (5) Supporting models and tools needed by industry for technology R&D and market transformation activities were validated. Attaining California’s clean energy goals require making a fundamental shift from today’s ad-hoc assemblages of static components to turnkey, intelligent, responsive, integrated building façade systems. These systems offered significant reductions in energy use, peak demand, and operating cost in California

Micro-Doppler Based Human-Robot Classification Using Ensemble and Deep Learning Approaches

Radar sensors can be used for analyzing the induced frequency shifts due to micro-motions in both range and velocity dimensions identified as micro-Doppler ($\boldsymbol{\mu}$-D) and micro-Range ($\boldsymbol{\mu}$-R), respectively. Different moving targets will have unique $\boldsymbol{\mu}$-D and $\boldsymbol{\mu}$-R signatures that can be used for target classification. Such classification can be used in numerous fields, such as gait recognition, safety and surveillance. In this paper, a 25 GHz FMCW Single-Input Single-Output (SISO) radar is used in industrial safety for real-time human-robot identification. Due to the real-time constraint, joint Range-Doppler (R-D) maps are directly analyzed for our classification problem. Furthermore, a comparison between the conventional classical learning approaches with handcrafted extracted features, ensemble classifiers and deep learning approaches is presented. For ensemble classifiers, restructured range and velocity profiles are passed directly to ensemble trees, such as gradient boosting and random forest without feature extraction. Finally, a Deep Convolutional Neural Network (DCNN) is used and raw R-D images are directly fed into the constructed network. DCNN shows a superior performance of 99\% accuracy in identifying humans from robots on a single R-D map.Comment: 6 pages, accepted in IEEE Radar Conference 201

UNMANNED GROUND VEHICLE (UGV) DOCKING, CONNECTION, AND CABLING FOR ELECTRICAL POWER TRANSMISSION IN AUTONOMOUS MOBILE MICROGRIDS

Autonomous Mobile Microgrids provide electrical power to loads in environments where humans either can not, or would prefer not to, perform the task of positioning and connecting the power grid equipment. The contributions of this work compose an architecture for electrical power transmission by Unmanned Ground Vehicles (UGV). Purpose-specific UGV docking and cable deployment software algorithms, and hardware for electrical connection and cable management, has been deployed on Clearpath Husky robots. Software development leverages Robot Operating System (ROS) tools for navigation and rendezvous of the autonomous UGV robots, with task-specific visual feedback controllers for docking validated in Monte-Carlo outdoor trials with a 73% docking rate, and application to wireless power transmission demonstrated in an outdoor environment. An “Adjustable Cable Management Mechanism” (ACMM) was designed to meet low cost, compact-platform constraints for powered deployment and retraction by a UGV of electrical cable subject to disturbance, with feed rates up to 1 m/s. A probe-and-funnel AC/DC electrical connector system was de- veloped for deployment on UGVs, which does not substantially increase the cost or complexity of the UGV, while providing a repeatable and secure method of coupling electrical contacts subject to a docking miss-alignment of up to +/-2 cm laterally and +/-15 degrees axially. Cabled power transmission is accomplished by a feed-forward/feedback control method, which utilizes visual estimation of the cable state to deploy electrical cable without tension, in the obstacle-free track of the UGV as it transverses to connect power grid nodes. Cabling control response to step-input UGV chassis velocities in the forward, reverse, and zero-point-turn maneuvers are presented, as well as outdoor cable deployment. This power transmission capability is relevant to diverse domains including military Forward-Operating-Bases, disaster response, robotic persistent operation, underwater mining, or planetary exploration

Flat-plate solar array project. Volume 5: Process development

The goal of the Process Development Area, as part of the Flat-Plate Solar Array (FSA) Project, was to develop and demonstrate solar cell fabrication and module assembly process technologies required to meet the cost, lifetime, production capacity, and performance goals of the FSA Project. R&D efforts expended by Government, Industry, and Universities in developing processes capable of meeting the projects goals during volume production conditions are summarized. The cost goals allocated for processing were demonstrated by small volume quantities that were extrapolated by cost analysis to large volume production. To provide proper focus and coverage of the process development effort, four separate technology sections are discussed: surface preparation, junction formation, metallization, and module assembly

Reflective Optics CPV Panels Enabling Large Scale, Reliable Generation of Solar Energy Cost Competitive With Fossil Fuels

The objective of this 18 month subcontract was the improvement of reflective optics CPV panels to enable the large-scale, reliable production of solar electricity to meet SAI-established LCOE targets, and ultimately provide a path to solar power at parity with or better than the cost of energy generated utilizing fossil fuels. To this end, SolFocus has completed this subcontract with great success as evidenced by the end results of a CPV panel with conversion efficiencies greater than the targeted 22% and manufacturing capabilities with a run rate capacity far exceeding the milestone benchmark \u3e3MW

Increasing the Efficiency and Productivity in the Production of Low Voltage Switchboard Using Resource Constrained Project Scheduling

Purpose: This research was made with the aim to give a scheduling proposal for the assembly activity and the proposal to allocate those activities into available resources using the resource-constrained project scheduling. Design/methodology/approach: The research begins with the problem exposition in the existing system on the Assembly Department of panel manufacturer company. To overcome the problem, several scheduling alternatives are formulated to yield better productivity. The performance of proposed system using RCPS is assessed using simulation method. Considering the circumstance of demand rate, the scenarios chosen are based on the parameter such as product departure cycle time, resource utilization, product output, and number of resource required. Findings: The scheduling alternatives provides the better arrangement of work elements in the assembly activity, especially for the product that represent the highest demand rate. For further research, the paper gives encouragement so that the application of RCPS can be used broader in the manufacturing area. Research limitations/implications: Although the findings were addressed to improve the existing system on the Assembly Department, the practical application haven’t been undergone and the performance assessment only based on the simulation method. Practical implications: By implementing the proposed scheduling, the company will experience several benefits. First, the company could increase its productivity by better utilization of its resources. Second, based on the simulation result, the company could avoid the backorder option while dealing with the high demand rate, and even can fully maximize its resource utilization without adding more worker or apply the overtime policy. Finally, the proposed scheduling that converted into the work instruction could help the company to perform the knowledge transfer from the existing worker or resigned worker to the newly-hired worker. Originality/value: The outcome of the research could become the guidance for other companies which have similar assembly system to apply the same method. This is the best paper that represents the application of RCPS in the large-sized component assemblies, where the walking worker is responsible for carrying tasks to each unmoved unit.Peer Reviewe

AI, Robotics, and the Future of Jobs

This report is the latest in a sustained effort throughout 2014 by the Pew Research Center's Internet Project to mark the 25th anniversary of the creation of the World Wide Web by Sir Tim Berners-Lee (The Web at 25).The report covers experts' views about advances in artificial intelligence (AI) and robotics, and their impact on jobs and employment

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd