Planning under uncertainty in resource-constrained systems

As autonomous systems become integrated into the real world, planning under uncertainty is a critical task. The real world is incredibly complex and systems must reason about factors such as uncertainty in their movements, environments, and human behavior. In the face of this uncertainty, agents must compute control trajectories and policies that enable them to maximize their expected performance while respecting probabilities on mission failure. The task is difficult because systems must reason about large numbers of scenarios concerning what may happen. Compounding the difficulty, many systems must reason about uncertainty while being resource-constrained. Autonomous cars and robots are time-limited because they must react quickly to their environment. Applications such as smart grids are computationally-limited because they require low-cost hardware in order to keep energy cheap.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Strawser, Daniel DeWitt.

Advisor dc:contributor.advisor

• Brian C. Williams.

• Mechanical Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123775

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123775