Christopher Vo

Christopher Vo

Washington DC-Baltimore Area
6K followers 500+ connections

About

I am an experienced robotics, autonomy, and artificial intelligence scientist with a…

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Experience

  • Saab, Inc. Graphic

    Saab, Inc.

    Washington, District of Columbia, United States

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    San Francisco, California, United States

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    Fairfax County, Virginia, United States

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    San Francisco, California, United States

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    Fairfax, VA

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    Harrisonburg, VA

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    Washington D.C. Metro Area

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    Washington D.C. Metro Area

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    Arlington, VA

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    Washington, District of Columbia, United States

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    Fairfax, Virginia, USA

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    Washington D.C. Metro Area

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    Reston, Virginia

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    Washington D.C. Metro Area

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Education

  • George Mason University Graphic

    George Mason University

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    Research in robotics, motion planning, and simulation. Recipient of the Ph.D. Presidential Fellowship (2007-2010), IMC Inc. Suneeth Nayak Scholarship Endowment, and several more scholarships.

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    Honors Program. Northern Virginia Technology Conference (NVTC) Bannister Scholar (2004 and 2005). Armed Forces Communications and Electronics Association (AFCEA) Scholar (2006). Graduated summa cum laude, BS in Computer Science (2006). Volgenau School of IT&E Most Outstanding Undergraduate (valedictorian) (2006). Perpetual Dean's List (all semesters). Computer Science Most Outstanding Graduate Student (2007).

Licenses & Certifications

Publications

  • Computing 3D From-Region Visibility Using Visibility Integrity

    IEEE Robotics and Automation Letters (RA-L)

    Visibility integrity (VI) is a measurement of similarity between the visibilities of regions. It can be used to approximate the visibility of coherently moving targets, called group visibility. It has been shown that computing visibility integrity using agglomerative clustering takes O(n4 log n) for n samples. Here, we present a method that speeds up the computation of visibility integrity and reduces the time complexity from O(n4 log n) to O(n2). Based on the idea of visibility integrity, we…

    Visibility integrity (VI) is a measurement of similarity between the visibilities of regions. It can be used to approximate the visibility of coherently moving targets, called group visibility. It has been shown that computing visibility integrity using agglomerative clustering takes O(n4 log n) for n samples. Here, we present a method that speeds up the computation of visibility integrity and reduces the time complexity from O(n4 log n) to O(n2). Based on the idea of visibility integrity, we show for the first time that the visibility-integrity roadmap (VIR), a data structure that partitions a space into zones, can be calculated efficiently in 3-D. More specifically, we offer two different offline approaches, a naive one and a kernel-based one, to compute a VIR. In addition, we demonstrate how to apply a VIR to solve group visibility and group following problems in 3-D. We propose a planning algorithm for the camera to maintain visibility of group targets by querying the VIR. We evaluate our approach in different 3-D simulation environments and compare it to other planning methods.

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  • A Comparative Study of Control Architectures in UAV/UGV-based Surveillance System

    In Proceedings of the 2014 Industrial and Systems Engineering Research Conferencee 2014 Winter Simulation Conference

  • A DDDAMS-Based UAV and UGV Team Formation Approach for Surveillance and Crowd Control

    Proceedings of the 2014 Winter Simulation Conference

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  • DDDAMS-based Crowd Control via UAVs and UGVs

    Proceedings of the 2013 International Conference on Computational Science

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  • Following a Group of Targets in Large Environments

    Proceedings of the Fifth International Conference on Motion in Games

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  • Group Following in Monotonic Tracking Regions

    Proceedings of the 22nd Fall Workshop on Computational Geometry

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  • Reusable Sampling-Based Techniques for Manipulation via Pushing

    Workshop on Progress and Open Problems in Motion Planning, joint with the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)

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  • Collaborative Foraging Using Beacons

    Proceedings of the Ninth International Conference on Autonomous Agents and Systems (AAMAS 2010)

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    • Brian Hrolenok
    • Keith Sullivan
    • Sean Luke
  • Following a Large Unpredictable Group of Targets Among Obstacles

    Proceedings of the Third International Conference on Motion in Games

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  • Following Multiple Unpredictable Targets Among Obstacles

    ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (i3D 2010)

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  • RoboPatriots: George Mason University 2010 RoboCup Team

    Proceedings of 2010 RoboCup Workshop

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  • Scalable and Robust Shepherding via Deformable Shapes

    Proceedings of the Third International Conference on Motion in Games

  • Visibility-Based Strategies for Searching and Tracking Unpredictable Coherent Targets Among Known Obstacles

    IEEE International Conference on Robotics and Automation (ICRA 2010) Workshop: Search and Pursuit/Evasion in the Physical World: Efficiency, Scalability, and Guarantees

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  • Behavior-Based Motion Planning for Group Control

    2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

  • Cooperative Coevolution and Univariate Estimation of Distribution Algorithms

    Proceedings of the 10th International Workshop on Foundations of Genetic Algorithms (FOGA 2009)

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  • RoboPatriots: George Mason University 2009 RoboCup Team

    Proceedings of 2009 RoboCup Workshop

    Other authors
    • Keith Sullivan
    • Brian Hrolenok

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