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dc.contributor.advisorRus, Daniela
dc.contributor.authorRay, Aaron Castagna
dc.date.accessioned2022-02-07T15:11:40Z
dc.date.available2022-02-07T15:11:40Z
dc.date.issued2021-09
dc.date.submitted2021-09-21T19:54:21.957Z
dc.identifier.urihttps://hdl.handle.net/1721.1/139901
dc.description.abstractWe seek to combine high level planning with low level reactive control to solve a variety of viewpoint-constrained target following tasks. In the scenarios we consider, a team of tracking agents is desired to gain some sort of visual information about one or more target agents. A high level planning algorithm accounts for coarse, global decisions, such as “Which targets should each tracker be responsible for?”, or “When should a tracker visit each target?” This level of planning is combinatorial in nature and requires coordination between the tracking agents. We combine this process with a lower-level reactive control accounts for stochastic target motion. By making this controller aware of a viewpoint cost function, the behavior of the tracking agents can be both more performant and easier to deploy on real robots.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright MIT
dc.rights.urihttp://rightsstatements.org/page/InC-EDU/1.0/
dc.titleViewpoint-Aware Task Planning and Model Predictive Control for Applications in Videography and Multi-Target Tracking
dc.typeThesis
dc.description.degreeS.M.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Science in Electrical Engineering and Computer Science


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