Viewpoint-Aware Task Planning and Model Predictive Control for Applications in Videography and Multi-Target Tracking
Name
Ray-aaronray-SM-EECS-2021-thesis.pdf
Description
Thesis PDF
Size
4.73 MB
Format
Adobe PDF
Checksum (MD5)
43e78ff5672631c46f659ba9bad3b852
Author(s)
Ray, Aaron Castagna
Advisor(s)
Rus, Daniela
Date Issued
September 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
