A Koopman-Based Reduced-Order State Observer for Visual Localization of Robots

A reduced-order observer using Koopman lifting linearization is developed for localization of a robot guided by a vision system. The Koopman operator is a powerful method for representing nonlinear robot dynamics as a linear model in a lifted space. Koopman faces two main challenges with robot localization. One is that the lifted linear system is not observable in general; standard Kalman filter and state observers cannot be applied to such non-observable systems. The other is that a large number of observables are required for accurate linearization. Here, we present 1) a new reduced-order state observer for a Koopman lifted linear model that satisfies the observability conditions, and 2) measurement of the multitude of Koopman observables by extracting many features from a camera image. These image features used as Koopman observables are directly measured in real-time and, thereby, make the observability matrix of the reduced-order state observer full rank. The method is developed for a robot crane system equipped with a vision system. We can estimate the endpoint of the robot using a reduced-order state observer of a lifted linear model where 20 observables are obtained from a visual image.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Williams, Jadal

Advisor dc:contributor.advisor

• Asada, H. Harry

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151814

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151814