Center for Automation, Robotics, and Distributed Intelligence

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Example CARDI Projects


Cooperative Beamforming for Efficient and Secure Wireless Communication


Beamform A network of nodes acting as a virtual antenna to beamform data back to some destination. A destination may be a base station or another cluster of nodes.

Project Team: Tracy Camp
Sponsor: National Science Foundation

There is a growing need for wireless networks that can sustain high data rates, are robust to interference, make efficient use of battery resources, and offer secure communications. This project introduces cooperative beamforming (CB), a novel technique that enables high throughput and power efficient communications in a secure manner. CB consists of two stages. In the first stage, the sources share their data with neighboring nodes via low-power communications. Various approaches for such information sharing are considered, with a goal to minimize queuing delays, conserve energy, and achieve high throughput. In the second stage, the cooperative nodes apply a weight to the signal received during first stage, and transmit. The weights are such that a specific objective criterion (e.g., signal to interference at the destination) is maximized. In CB, although each node uses low power, all nodes together can deliver high power to a faraway destination. This increase in power offsets power reduction due to propagation attenuation. CB can be viewed as an alternative to multihop transmission and, unlike multihop transmission, does not deplete the power resources of other nodes. Since CB can achieve long distance communication, new paths can be found to improve the overall network performance. Also, CB improves network security by avoiding eavesdroppers; unlike traditional cryptographic-based protocols that operate at higher layers and are sensitive to the broadcast nature of the transmission medium, CB improves security at the physical layer. CB will be implemented on a hardware network testbed to demonstrate how the developed techniques can revolutionize wireless communications.


Cyber-Enabled Efficient Energy Management of Structures (CEEMS)

CPS: Medium: Cyber-Enabled Efficient Energy Management of Structures (CEEMS)



Principal Investigator: Tyrone Vincent
Co-Principal Investigators: Kevin Moore, Dinesh Mehta, Marcelo Simoes, Robert Braun
Sponsor: National Science Foundation

The objective of this research is the development of methods for the control of energy flow in buildings, as enabled by cyber infrastructure. The approach is inherently interdisciplinary, bringing together electrical and mechanical engineers alongside computer scientists to advance the state of the art in simulation, design, specification and control of buildings with multiple forms of energy systems, including generation and storage. A significant novelty of this project lies in a fundamental view of a building as a set of overlapping, interacting networks. These networks include the thermal network of the physical building, the energy distribution network, the sensing and control network, as well as the human network, which in the past have been considered only separately. This work thus seeks to develop methods for simulating, optimizing, modeling, and control of complex, heterogeneous networks, with specific application to energy efficient buildings. The advent of maturing distributed and renewable energy sources for on-site cooling, heating, and power production and the concomitant developments in the areas of cyberphysical and microgrid systems present an enormous opportunity to substantially increase energy efficiency and reduce energy-related emissions in the commercial building energy sector. In addition, there is a direct impact of the proposed work in training students with backgrounds in the unique blend of engineering and computer science that is needed for the study of cyber-enabled energy efficient management of structures, as well as planned interactions at the undergraduate and K-12 level.


Enabling Automated Oil and Gas Processes Using Unmanned Mobile Robots

PI: Qi Han
Co-PI: John Steele
Sponsor: Petroleum Institute, UAE

The presence of toxic gasses (such as H2S) and very high outdoor temperatures in the Middle East region poses great risks to staff on the site of oil and gas facilities. In order to improve safety and increase reliability of oil and gas processes such as inspection, operations and maintenance of oil and gas facilities, this project seeks to automate these tasks using unmanned mobile robots. More specifically, this project conducts research in two areas. The first area is to design and develop a mobile platform, and then design techniques for manipulation. The second area is to develop techniques for navigation, communication, and sensing of the robot.

Extracting Layer Parameters from Intelligent Compaction Measurements

Extracting Layer Parameters from Intelligent Compaction Measurements



Principal Investigators: Michael Mooney and Robert Rinehart

As detailed in a recent Geotechnique paper, current intelligent compaction (IC) rollers provide a measure of soil (ground) stiffness representative to a depth of approximately 0.8-1.2 m. Given typical layered earthwork situations, IC rollers provide a measure of composite stiffness that is representative of multiple layers, as described in this J. Geotech. & Geoenv. Engineering paper. This research seeks to extract layered elastic moduli from composite roller stiffness measurements. The research involves forward boundary element and finite element modeling of the roller-soil system coupled with inversion to estimate layer parameters. The use of boundary/finite element models for real-time inversion is impractical. Therefore, our research is also pursuing the training of statistical forward models from the boundary/finite element model data. We anticipate that the research will produce an algorithm that will integrate with existing commercially available IC measurement systems.


A Heterogeneous Networking Test Bed to Support Middleware Services for Pervasive Sensing

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Principle Investigator: Qi Han
Sponsor: National Science Foundation

Numerous interesting applications have been enabled by embedded sensing technologies and significant research progress on wireless sensor networks. To further ensure a wider adoption of this emerging technology, seamless integration of wireless sensor networks with other existing networks such as WiFi and the Internet is a must. In order to address challenges that arise from such an integrated infrastructure, this project builds HeteroNet, a heterogeneous networking infrastructure, by augmenting an existing flat and homogeneous sensor network test bed. HeteroNet integrates resource constrained sensor nodes and more powerful sensing devices, stationary nodes, mobile nodes, and resource sufficient servers. These nodes communicate in wireless or wired fashion. This test bed establishes an experimental infrastructure to serve as a platform for development, testing, validation, and evaluation of the investigator's current research on middleware services for emerging applications on hybrid networks. Research benefiting from HeteroNet includes: integration of interoperable sensor networks to the Internet, amorphous event monitoring in sensor networks, and system status monitoring for QoS-aware mobile applications.

HeteroNet enables research that is not possible via simulation or current small homogeneous sensor network test bed. The findings from the research enabled by HeteroNet have a profound impact on pushing the state-of-the-art of next-generation distributed systems and networks. The development of HeteroNet also benefits educational activities at the graduate and undergraduate levels. HeteroNet is used to facilitate and improve courses on networking, distributed systems, multimedia systems, and computer architectures at the graduate and undergraduate levels.


Intelligent Geosystems


Intelligent Geosystems Intellilgent Geosystems 2

Principal Investigator: Michael Mooney
Co-Principal Investigators: Linda Figueroa, Tracy Camp, Jason Delborne, Andre Revil
Sponsor: National Science Foundation

This Integrative Graduate Education and Research (IGERT) award supports a Ph.D. training program at the Colorado School of Mines to pursue integrative research and education in Intelligent Geosystems. Graduate students will be trained to add real time, adaptive, sensing capabilities to the monitoring of natural or engineered earth structures, e.g., an earth dam, a ground water system, or a geoconstruction site (tunneling, urban excavation, highway); the sensor networks employed will allow a geosystem to sense its environment, diagnose its condition, and make decisions to improve the management, operation, or objective of the geosystem. The goals are to advance the development of the "intelligent" geosystems while educating and training a new generation of leaders who are able to operate effectively in this emerging interdisciplinary area. The proposed IGERT program will institute an interdisciplinary and holistic approach to trainees. Key components of this IGERT award include: (1) a multi-disciplinary collaborative research team framework to foster team development and interdisciplinary innovation in intelligent geosystem concepts; (2) a leadership and teamwork development program to train the next generation of geosystem leaders for industry, academia and government; (3) a PhD minor in social/environmental ethics & policy to broaden trainee understanding beyond the technical challenges to the social, environmental and political aspects of intelligent geosystems; (4) a self-paced cross-disciplinary technical course using modules in intelligent geosystems; and (5) an internship with a government laboratory or industry in intelligent geosystems. These five components of this IGERT program will produce diverse, highly skilled leaders with the strong social and environmental awareness required in multidisciplinary environments. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.
For more information, please visit http://smartgeo.mines.edu/.


Leveraging Low-Dimensional Structure for Time Series Analysis and Prediction

Leveraging Low-Dimensional Structure for Time Series Analysis and Prediction



Project Team: Mike Wakin
Sponsor: National Science Foundation

Predicting the behavior of complex systems is central to many tasks of great scientific and national importance, including arenas such as meteorology, financial markets and global conflict. Modern science is ingrained with the premise that repeated observations of a dynamic phenomenon can help in understanding its driving mechanisms and predicting its future behavior. The investigators study methods for improving our ability to characterize and predict such systems even when they are very large (i.e., with many interacting factors) or appear highly unordered (i.e., chaotic systems). This research leverages new mathematical results that enable analysts to efficiently capture the simple structure that is often present even in systems that appear very complex. These results lead to improvements and performance guarantees for heuristic prediction methods based on artificial neural networks, which are often used in practice but can sometimes fail inexplicably.

Time series prediction is often approached by postulating a structured model for a hidden system driving data generation. This project borrows from recent advances in low-dimensional signal modeling to advance the state of the art in time series analysis and prediction tools when similar low-dimensional structure is present. For linear systems, this research develops efficient estimation strategies that improve upon classical techniques by encouraging sparse solutions. For nonlinear models, this project builds upon Takens' Embedding Theorem, which states that the image of an attractor manifold can be reconstructed using a sequence of time series observations, to guarantee a quantifiably stable embedding of the attractor manifold. Furthermore, this research aims to improve upon and make performance guarantees for reservoir computing methods, where randomly-connected neural networks have been identified as effective mechanisms for predicting chaotic time series.


Maximizing Wind Farm Energy Production Using Coordinated Turbine Control

Maximizing Wind Farm Energy Production Using Coordinated Turbine Control



Project Team: Kathryn Johnson
Sponsor: National Science Foundation

Wind farms are becoming more widely used around the country and the globe with the goal of producing cleaner energy more cost effectively. One of the problems encountered in such wind farms is the aerodynamic interaction among turbines that causes a decrease in the total energy extracted from the wind when compared to an equal number of individual turbines operating under the same wind input conditions as the wind farm. The focus of this research is the development of a wind farm simulation model and extremum seeking controller (ESC) that account for and mitigate this aerodynamic interaction among turbines. The simulation model that has been developed that can model the power produced by turbines on a wind farm as wind speed and direction change. The control input to each turbine is the axial induction factor of each turbine, which can be controlled using each turbine’s pitch angle and tip-speed ratio. Unfortunately, the ESC results are not consistent or conclusive. More specifically, we have shown that ESC, using each turbine’s axial induction factors as control variables, can improve the energy capture of our specific wind farm under specific turbine configuration and wind input conditions. However, in other cases total wind farm energy is decreased. The full explanation for increases and decreases is not yet fully understood, and this research has inspired many future avenues for research, including the effect of many different number of turbines and wind farm layouts, the ideal ESC frequency design based on multiple distinct time constants throughout the system, the creation of a set of guidelines for tuning ESC parameters, the optimum ESC bypass structure, and the effect of simulation duration on ESC results.


New Theory and Algorithms for Scalable Data Fusion

New Theory and Algorithms for Scalable Data Fusion



Project Team: Mike Wakin
Sponsor: Air Force Office of Scientific Research

Recent developments in sensor technology, signal processing, and wireless communications have enabled the conception and deployment of large-scale networked sensing systems comprising coordinated stationary and mobile platforms carrying sensors of diverse modalities. The promise of systems lies in their ability to intelligently make decisions by integrating information from massive amounts of sensor data. However, this great promise is offset by a number of critical challenges, which include growing volumes of sensor data, increasingly diverse data, diverse and changing operating conditions, novel information appearing, and increasing mobility of sensor platforms. This project aims to develop a principled theory of data fusion and decision making that provides predictable, optimal performance for a range of different problems through the effective utilization of the available network of resources.


Process Control for Low-Cost Electrochromic Film on Plastic


Process Control for Low-Cost Electrochromic Film on Plastic



Principal Investigator: Tyrone Vincent
Sponsor: ITN Energy Systems/Department of Energy

Electrochromic windows have the ability to darken or lighten in response to an electric signal, and if broadly used in buildings they would have the potential to greatly cut back cooling costs by reducing the amount of radiant energy entering the building. Unfortunately, with current manufacturing processes, electrochromic windows remain too expensive for widespread use. With support from the Department of Energy, ITN Energy Systems of Littleton CO is developing a low cost manufacturing process based on a wide-web, continuous processing sputtering systems. CSM is working with ITN Energy Systems on the control systems for this process, as repeatable processes with good cross-web uniformity are key to high yields, and thus low cost manufacturing.

Colorado School of Mines