2026-09-21T09:23:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1452212025-10-30T17:51:27Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Sangbae Kim.
Roudebush, George Imre.
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2022-08-31T16:13:56Z
2022-08-31T16:13:56Z
2020
2020
https://hdl.handle.net/1721.1/145221
1342119915
Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2020
Cataloged from the official PDF of thesis. "Due to the condition of the original material, there are unavoidable flaws in this reproduction. We have made every effort possible to provide you with the best copy available. The images contained in this document are of the best quality available"--Disclaimer Notice page.
Includes bibliographical references (pages 61-62).
Controllers for dynamic robots which regulate around an optimized trajectory often struggle with reliability in uncertain environments, and making control decisions in real-time. Using a map of risk to the robot's state, a risk gradient controller can be created to find paths back to safety from anywhere in the robot's reachable space. Despite generating a variety of complex and dynamic behaviors, this method suffers from sensitivity to sensor and process noise, as the value of risk associated with the robot's state is not well behaved. In order to correct for this sensitivity, a Stochastic Risk Gradient Controller (RGC) and sample-based Extended Kalman Filter (EKF) are proposed. The Sample Based EKF uses pre-simulated dynamics to generate optimal state and uncertainty estimate up to 10 times faster than an online-simulation. The Stochastic RGC then uses those estimates to calculate more robust control actions in real-time. This framework is applied to a model of a pogo-stick robot and simulated with various levels of sensor and process noise. In trials with relatively large measurement noise, the Stochastic Risk Gradient controller succeeded up to 15% more often than the naive risk gradient controller, while failing up to 8% more often in cases when measurement noise was relatively low.
by George Imre Roudebush.
S.B.
S.B. Massachusetts Institute of Technology, Department of Mechanical Engineering
62 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Risk based control in uncertain environments
Thesis
application/pdf
2022-08-31T16:13:55Z
Publication
Bachelor
unknown
unknown
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<Abstract>Controllers for dynamic robots which regulate around an optimized trajectory often struggle with reliability in uncertain environments, and making control decisions in real-time. Using a map of risk to the robot's state, a risk gradient controller can be created to find paths back to safety from anywhere in the robot's reachable space. Despite generating a variety of complex and dynamic behaviors, this method suffers from sensitivity to sensor and process noise, as the value of risk associated with the robot's state is not well behaved. In order to correct for this sensitivity, a Stochastic Risk Gradient Controller (RGC) and sample-based Extended Kalman Filter (EKF) are proposed. The Sample Based EKF uses pre-simulated dynamics to generate optimal state and uncertainty estimate up to 10 times faster than an online-simulation. The Stochastic RGC then uses those estimates to calculate more robust control actions in real-time. This framework is applied to a model of a pogo-stick robot and simulated with various levels of sensor and process noise. In trials with relatively large measurement noise, the Stochastic Risk Gradient controller succeeded up to 15% more often than the naive risk gradient controller, while failing up to 8% more often in cases when measurement noise was relatively low.</Abstract>
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