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Research & Development Areas

R&D

 

ADVANCED TECHNOLOGIES FOR CONTEMPORARY PROBLEMS AND CRITICAL APPLICATIONS

AGNC is a dynamic organization that strives for innovations and the infusion of newly emerging technologies within a variety of systems. Starting as a guidance and navigation company, our efforts have expanded into multiple complementary areas with the primary goals of increasing reliability, enhancing autonomy, and obtaining a better understanding of systems. Critical and complex systems are the primary target in AGNC's applications. AGNC's extensive R&D approach blends five areas of work encompassing guidance, navigation, control, and communications; complex systems analysis; unmanned systems and robotics; systems health monitoring and smart sensors; intelligent systems, computer vision, and neurocomputing

Research and Development Areas

Guidance, Navigation, Control, and Communications (GNCC)

American GNC Corporation is actively involved in pioneering efforts related to inertial sensors, interruption-free positioning, and INS/GNSS fusion. Since its establishment in 1986, AGNC has been actively engaged in the development of advanced Guidance, Navigation, Control, and Communications and the automation and integration for autonomous vehicles, robotics, ground vehicles, aircraft, marine vehicles, unmanned aerial systems, missiles, spacecraft, and satellites. Inertial navigation devices comprise the core navigation technology which is expanded with GPS/INS integration schemes, vision-based guidance (image processing), advanced filtering, simultaneous localization and mapping (SLAM), among others. AGNC produced the world’s first MEMS rate integrating gyroscope in 1999, setting the stage for continued development of its coremicroŽ IMU product series, and is among the very first companies to patent micro-electromechanical (MEMS) Inertial Measurement Unit (IMU) technology, which is commonly found in most handheld consumer electronics such as tablets and smartphones.  AGNC also has a rich history in the utilization of advanced control algorithms (fuzzy logic control, neural network based controllers, robust and adaptive control) for a variety of vehicle platforms. AGNC then expanded these GNC efforts to also include the communications field (hence, GNCC) where current areas of work involve: optimized routing protocols, mesh networks, low power wireless networks, cellular networks, high speed data links, etc.

Intelligent Systems, Computer Vision, and Neuroscience

  •  Intelligent Systems

Artificial Intelligence techniques enable the realization of cognitive systems, where involved methodologies include: vision systems, stochastic expert systems, Bayesian leaning, Artificial Neural Network paradigms, cognitive processing, relational reasoning, planning and scheduling, distributed software architectures, and agents design. Leveraging high level cognition functions for aiding the decision support process (such as tactical decisions in military applications and troubleshooting in the PHM field) and workflow analysis are key activities at AGNC. In addition, our vision systems provide machine perception, where advanced digital image processing techniques are blended with intelligent pattern recognition methods for highly accurate and real-time analysis.

  • Computer Vision

Everyday vision-based tasks of humans such as recognizing familiar places, driving a car, or reading another person’s expression may seem trivial. However, there are significant technological barriers for implementing such capabilities in computer vision systems. AGNC is dedicated to developing novel image processing solutions and has in-depth experience in related areas such as image enhancement, visual odometry, scene understanding, segmentation, and target detection & tracking

  • Neuroscience

Due to the current challenges within the neuroscience field, there are still several areas of opportunity for new innovations that can have a beneficial impact on system autonomy, performance, and reliability. AGNC is actively developing technologies that address the science and engineering aspects of new learning methods (including architectures, learning theory, analysis of network dynamics, self-organization, cognitive science, computational learning, genetic algorithms, and machine learning) for a wide range of applications (such as image processing, computer vision, diagnostics, prognostics, control, robotics, optimization, scheduling, resource allocation, signal processing, forecasting, among others). Emerging trends for contemporary problems are always in sight for the integration of cutting edge innovative technologies such as deep learning applied to cognitive image processing analysis and health monitoring systems.

Advanced Modeling and Automated Complex Systems Analysis

Complex systems engineering deals with understanding the sophisticated interrelations among systems, subsystems, and components, where even simple design changes can have a significant impact throughout these elements. Advanced modeling that takes into consideration an element's healthy and degradation effects combined with stochastic techniques and state-of-the-art health monitoring for automated analysis together provide a road-map for addressing complex systems analysis and visualization in a complete way. As such, AGNC is developing new tools that will improve the design process by making the interactions among a multitude of subsystems and components clear and also allow for reducing the semantic gap among engineers across multiple disciplines.

Unmanned Systems and Robotics

Enhancing system autonomy by infusing technologies that compile advanced robust control, navigation architectures, and health monitoring is a key focus at AGNC. Areas of work include: (a) design of image processing embedded systems and payloads for drones with minimized size, weight, power consumption and cost (SWAP-C); (b) robust low level control implementations; (c) robot virtual reality; (d) advanced system simulation; (e) sensor failure detection by the analysis of system redundancies; and (f) high level cognitive functions applied to navigation, mapping, vision, information abstraction, etc. The AGNC coremicro Robots serve as ideal platforms for integrating new technologies to reduce operator workload and increase autonomy and include target detection and tracking, obstacle avoidance, localization, terrain mapping, navigation and route formulation, resource allocation, among others. However, the developed technologies can be applied across a wide range of other systems as well, where specific applications include: (i) UAV based surveillance and recognition; (ii) surgical support by miniaturized robots; (iii) layered sensing architectures for military command and control; and (iv) terrain analysis and classification by computer vision.

System Health Monitoring and Smart Sensors

  • Prognostics and Health Monitoring (PHM)

PHM optimizes system reliability and supports maintenance operations (Condition Based Maintenance, CBM) as well as automated logistics (depot management and the supply chain). Depending on the application, PHM realizations involve: sensing technologies, distributed architectures, system modeling, failure analysis and characterization, robust pattern recognition techniques, and regression. AGNC is involved in enhancing PHM technologies by: (a) blending complex system modeling techniques with cognitive analysis based on cutting edge Neuroscience and Artificial Intelligence techniques; (b) integration of innovative sensor-self-diagnostics schemes for enhanced transducer monitoring, reliability, and safe operation; (c) developing distributed health monitoring algorithms within smart sensor networks implementations; (d) conducting an integral approach where processes at the system level and sensor level provide system health analysis granularity as well as sensor data validation and data fusion at different complex system levels; and (e) developing standardized system health monitoring frameworks looking to facilitate retrofitting, system upgrades, and integration. We are committed to advancing the state-of-the-art in PHM related areas such as structural health monitoring (SHM) and integrated vehicle health management (IVHM).

  • Smart Sensors

For complex infrastructures where technology continuously evolves, the integration of configurable and standardized smart sensors with embedded data acquisition, flexible wired/wireless communications, failure awareness, self-identification (using Transducer Electronic Data Sheets), self-learning, and embedded intelligence provide a strategy to facilitate technological upgrades, increase system reliability, reduce operator work-load, and increase modularity, scalability, and extensibility. AGNC research efforts have produced a "Distributed Intelligent Health Monitoring" framework that consists of Smart Sensors Networks (SS-Net) serving as distributed computational platforms that can host intelligent elements (processes) to improve monitoring operations. For our smart sensors, design for optimized size, weight, power consumption and cost (SWaP-C) is often a constraint, such as in aerospace applications. Standalone devices designed for energy harvesting constraints are also a current need. Current and ongoing strategic areas of work are focused to: (a) ruggedization for operation in pressurized, sea, aerospace, and cryogenic environments; (b) development of non-invasive data and energy transfer through barriers (aluminum, steel, and composites) by state-of-the-art ultrasonic techniques to maintain the host structure’s integrity; (c) standardizing communications and system architectures; (d) infusing networking capability and the newest smart technologies (smart-phones, tablets, etc.); and (e) integration of MIL and NEMA standards.

  • Enterprise Architectures for Large-Scale-Data Processing

Areas of work are related to the advancement of: (i) web-based server containing a database for managing system health information; (ii) browser-based remote mobile client software for desktop PCs, laptops, tablets, and smart phones that provides system health data, information, and knowledge (DIaK); (iii) automated maintenance guidance to e.g. technicians; (iv) system anomaly awareness to both clients and main monitoring consoles; (v) secure networking communications; and (vi) close coupling of smart sensor networks to enable automated monitoring capability and ubiquitous system health data visualization. A key focus is to synergistically integrate information technologies, diagnostics, monitoring and PHM systems, smart mobile technologies, and secure communications. Target application examples are:

- Transportation systems (oil, gas, and water distribution systems) monitoring
- Refineries pipelines, tanks, and processing systems monitoring
- Computerized Maintenance Management Systems (CMMS)
- Complex Systems Maintenance and Repair Guidance
- Logistics and Depot Maintenance

  •   Real Time Processing

Delivering high performance computational platforms (in standalone or networked implementations) that enable real time processing while meeting additional design constraints is of paramount importance for a variety of applications including efficient data acquisition, control, and high level processing. Distributed hardware and software architectures, the IEEE 1451 smart sensor standards, standalone systems, and custom hardware design (DSPs, FPGAs, and ultra-low-power microcontrollers) are baseline capabilities, that when combined with high end embedded processing devices provide the building blocks for advanced system realizations that meet the real time processing requirement. Focus is provided to system designs with optimized SWAP-C for monitoring applications, smart sensors, drones, advanced communication bridges, image processing hardware, and computational platforms for deep learning.

 

Alliances and Collaborators

Children's National Medical Center

Georgetown University

Louisiana Tech University

Pennsylvania State University - Applied Research Laboratory

Rensselaer Polytechnic Institute

The University of Texas at Arlington

University at Albany

University of Kansas

University of Southern California

Virginia Tech

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