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dc.contributor.authorKragelund, Sean
dc.dateWednesday, 12 April 2017
dc.date.accessioned2017-05-18T00:23:53Z
dc.date.available2017-05-18T00:23:53Z
dc.date.issued2017-04-12
dc.identifier.urihttp://hdl.handle.net/10945/53373
dc.descriptionTechCon2017 (CRUSER)en_US
dc.descriptionPresented by Dr. Sean Kragelund: NPS Mechanical & Aerospace Eng.en_US
dc.descriptionIncludes slidesen_US
dc.description.abstractNetworked autonomous vehicles have great potential in a wide range of littoral sensing applications, including mine countermeasures (MCM), undersea warfare (USW), and intelligence, surveillance, and reconnaissance (ISR) missions. Motion planning algorithms which consider the capabilities and limitations of individual vehicle/sensor configurations are a key enabler for optimal employment of dissimilar vehicles to accomplish a given sensing objective. Optimal control is a model-based framework for solving motion planning problems with multiple vehicles, dynamic constraints, and complex performance objectives; although they usually require numerical solutions and deterministic formulations. Recent research at NPS, however, has produced a general mathematical and computational framework for numerically solving these problems, even in the face of parameter uncertainty: Generalized Optimal Control (GenOC). GenOC has been used to solve complex motion planning problems with multi-agent interactions, in applications ranging from optimal search to swarm defense and target herding behaviors. This presentation describes recent CRUSER supported research which utilized the GenOC framework to generate optimal search trajectories for multiple, dissimilar vehicles conducting MCM with different sonar systems. The resulting trajectories have been shown to outperform traditional lawnmower coverage patterns when detecting mines under time or resource constraints. Moreover, the ability to rapidly solve optimal search problems in this framework can establish performance benchmarks and provide important insights into optimal sensor and vehicle employment strategies. These capabilities are being developed into an experimental mission planning and analysis tool for the MCM community. We will also highlight how GenOC will be used to generate optimal vehicle trajectories for aerial, surface, and underwater vehicles during the upcoming CRUSER/JIFX multi-threaded experiment (MTX) at San Clemente Island in August, 2017. ScanEagle UAV trajectories will be devised to provide persistent aerial surveillance and mesh network connectivity in support of blue team operations, while also guarding against potential red team incursions. Meanwhile, SeaFox USV trajectories will implement search patterns to detect surface threats with radar, while relaying communications between ScanEagle aircraft and REMUS UUVs executing optimal sonar search patterns.en_US
dc.description.sponsorshipNPS CRUSERen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleGeneralized Optimal Control for Networked Autonomous Vehicles in Uncertain Domains [video]en_US
dc.typeVideoen_US
dc.typePresentationen_US
dc.contributor.departmentMechanical and Aerospace Engineering (MAE)


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