2026-09-19T05:57:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1518142023-08-24T03:53:12Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Asada, H. Harry
Williams, Jadal
Massachusetts Institute of Technology. Department of Mechanical Engineering
2023-08-23T16:10:47Z
2023-08-23T16:10:47Z
2023-06
2023-07-19T18:45:48.713Z
https://hdl.handle.net/1721.1/151814
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.
S.M.
Massachusetts Institute of Technology
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A Koopman-Based Reduced-Order State Observer
for Visual Localization of Robots
Thesis
application/pdf
Master
Master of Science in Mechanical Engineering
Publication
unknown
unknown
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<Abstract>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.</Abstract>
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