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dc.contributor.authorHow, Jonathan P.
dc.descriptionPresented on January 22, 2020 at 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.en_US
dc.descriptionJonathan P. How is the Richard C. Maclaurin Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He is a Fellow of both the IEEE and the American Institute of Aeronautics and Astronautics (AIAA), the editor-in-chief of the IEEE Control Systems magazine, and an associate editor of the AIAA Journal of Aerospace Information Systems and the IEEE Transactions of Neural Networks and Learning Systems (TNNLS). How has won multiple AIAA best paper awards, including those of 2011, 2012, and 2013. Additionally, he earned the 2011 IFAC Automatica award for the best application paper, the 2020 AIAA Intelligent Systems Award, and 2002 Institute of Navigation Burka Award.en_US
dc.descriptionRuntime: 55:45 minutesen_US
dc.description.abstractOur work addresses the planning, control, and mapping issues for autonomous robot teams that operate in challenging, partially observable, dynamic environments with limited field-of-view sensors. In such scenarios, individual robots need to be able to plan/execute safe paths on short timescales to avoid imminent collisions. Performance can be improved by planning beyond the robots’ immediate sensing horizon using high-level semantic descriptions of the environment. For mapping on longer timescales, the agents must also be able to align and fuse imperfect and partial observations to construct a consistent and unified representation of the environment. Furthermore, these tasks must be done autonomously onboard, which typically adds significant complexity to the system. This talk will highlight four recently developed solutions to these challenges that have been implemented to (1) robustly plan paths and demonstrate high-speed agile flight of a quadrotor in unknown, cluttered environments; (2) certify safety in learning-based methods in presence of perturbation in observations; (3) plan beyond the line-of-sight by utilizing the learned context within the local vicinity, with applications in last-mile delivery; and (4) correctly synchronize partial and noisy representations and fuse maps acquired by (single or multiple) robots using a multi-way data association algorithm.en_US
dc.format.extent55:45 minutes
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectData associationen_US
dc.subjectMultiagent planningen_US
dc.titleNavigation and Mapping for Robot Teams in Uncertain Environmentsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameMassachusetts Institute of Technologyen_US

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  • IRIM Seminar Series [101]
    Each semester a core seminar series is announced featuring guest speakers from around the world and from varying backgrounds in robotics.

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