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dc.contributor.authorRogers, Jonathan
dc.date.accessioned2015-04-23T19:00:37Z
dc.date.available2015-04-23T19:00:37Z
dc.date.issued2015-04-15
dc.identifier.urihttp://hdl.handle.net/1853/53313
dc.descriptionPresented on April 15, 2015 at 12:00 p.m. at the Manufacturing Related Disciplines Complex (MRDC), GTMI Auditorium.en_US
dc.descriptionJonathan Rogers is an assistant professor in the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology, where he is also director of the Intelligent Robotics and Emergent Automation Laboratory (iREAL). Previously, he served as an assistant professor of Aerospace Engineering at Texas A&M University. Roger's research interests lie at the intersection of nonlinear dynamics, robust control, and state estimation. He received his PhD in Aerospace Engineering from Georgia Tech in 2009, and a BS in Physics from Georgetown University in 2006. In 2011, he was selected as an Army Research Office Young Investigator for his work in state estimation for complex dynamical systems.
dc.descriptionRuntime: 58:08 minutes
dc.description.abstractRisk is a ubiquitous aspect of control and path planning for robots operating in unstructured real‐world environments. Nevertheless, humans still far surpass robots in their ability to evaluate complex tradeoffs under uncertainty through risk analysis and subsequent decision‐making. Many traditional approaches to the stochastic optimal control problem, such as Partially Observable Markov Decision Processes (POMDP’s), suffer from the curse of dimensionality and become computationally intractable in many real-world scenarios. In this seminar, a new class of stochastic control algorithms is proposed that makes use of emerging high‐performance computing devices, specifically GPUs, to perform real‐time uncertainty quantification (UQ) as part of a feedback control loop. These algorithms propagate the time‐varying probability density of the robot state and optimize control actions with respect to accuracy, obstacle avoidance, and other criteria. Key to practical implementation of these algorithms is the fact that many UQ algorithms can be parallelized; thus they can leverage emerging embedded high‐throughput devices for real‐time or near real‐time execution. Following an overview of the general formulation of these stochastic control algorithms, examples are provided in the form of autonomous parafoil and quadrotor flight controllers that make use of real‐time uncertainty analysis for obstacle avoidance in constrained environments. Recent experimental flight tests using embedded GPUs show that a strong coupling between UQ and optimal control offers a practical solution for risk mitigation by autonomous systems.en_US
dc.format.extent00:00 minutes
dc.format.extent58:08 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesIRIM Seminar Seriesen_US
dc.subjectRoboticsen_US
dc.subjectUncertaintyen_US
dc.titleRisky Robotics: Developing a Practical Solution for Stochastic Optimal Controlen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machineen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Mechanical Engineeringen_US
dc.embargo.termsnullen_US


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  • IRIM Seminar Series [112]
    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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