Publication:
Optimal sensor-based motion planning for autonomous vehicle teams

dc.contributor.advisorKaminer, Isaac I.
dc.contributor.authorKragelund, Sean P.
dc.contributor.departmentMechanical and Aerospace Engineering (MAE)
dc.dateMar-17
dc.date.accessioned2017-05-10T16:31:42Z
dc.date.available2017-05-10T16:31:42Z
dc.date.issued2017-03
dc.descriptionReissued 30 May 2017 with correction to student's affiliation on title page.
dc.description.abstractAutonomous vehicle teams have great potential in a wide range of maritime sensing applications, including mine countermeasures (MCM). A key enabler for successfully employing autonomous vehicles in MCM missions is motion planning, a collection of algo-rithms for designing trajectories that vehicles must follow. For maximum utility, these algorithms must consider the capabilities and limitations of each team member. At a minimum, they should incorporate dynamic and operational constraints to ensure trajectories are feasible. Another goal is maximizing sensor performance in the presence of uncertainty. Optimal control provides a useful frame-work for solving these types of motion planning problems with dynamic constraints and di_x000B_erent performance objectives, but they usually require numerical solutions. Recent advances in numerical methods have produced a general mathematical and computational framework for numerically solving optimal control problems with parameter uncertainty—generalized optimal control (GenOC)— thus making it possible to numerically solve optimal search problems with multiple searcher, sensor, and target models. In this dissertation, we use the GenOC framework to solve motion planning problems for di_x000B_erentMCMsearch missions conducted by autonomous surface and underwater vehicles. Physics-based sonar detection models are developed for operationally relevant MCM sensors, and the resulting optimal search trajectories improve mine detection performance over conventional lawnmower survey patterns—especially under time or resource constraints. Simulation results highlight the flexibility of this approach for optimal mo-tion planning and pre-mission analysis. Finally, a novel application of this framework is presented to address inverse problems relating search performance to sensor design, team composition, and mission planning for MCM CONOPS development.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.
dc.description.urihttp://archive.org/details/optimalsensorbas1094553003
dc.identifier.urihttps://hdl.handle.net/10945/53003
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.rightsCopyright is reserved by the copyright owner.en_US
dc.subject.authoroptimal controlen_US
dc.subject.authoroptimal searchen_US
dc.subject.authormine countermeasuresen_US
dc.subject.authormotion planningen_US
dc.subject.authorautonomous vehiclesen_US
dc.subject.authorunmanned vehiclesen_US
dc.subject.authorunmanned surface vesselen_US
dc.subject.authorautonomous underwater vehicleen_US
dc.subject.authorsonaren_US
dc.subject.authordetection modelsen_US
dc.subject.authormission planningen_US
dc.titleOptimal sensor-based motion planning for autonomous vehicle teamsen_US
dc.typeThesisen_US
dspace.entity.typePublication
etd.thesisdegree.disciplineMechanical Engineeringen_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.nameDoctor of Philosophy In Mechanical Engineeringen_US
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