dc.contributor.advisor | Rus, Daniela | |
dc.contributor.author | Ray, Aaron Castagna | |
dc.date.accessioned | 2022-02-07T15:11:40Z | |
dc.date.available | 2022-02-07T15:11:40Z | |
dc.date.issued | 2021-09 | |
dc.date.submitted | 2021-09-21T19:54:21.957Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/139901 | |
dc.description.abstract | We 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.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | Copyright MIT | |
dc.rights.uri | http://rightsstatements.org/page/InC-EDU/1.0/ | |
dc.title | Viewpoint-Aware Task Planning and Model Predictive Control for Applications in Videography and Multi-Target Tracking | |
dc.type | Thesis | |
dc.description.degree | S.M. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Science in Electrical Engineering and Computer Science | |