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Model-Based Unified State and Phase Estimation for Torque Actuated Dissipative Spring-Mass Runner Using Limited Sensory Information
Date
2024-01-01
Author
KARAGÖZ, OSMAN KAAN
Kılıç, Ayşegül
Ankaralı, Mustafa Mert
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In the field of autonomous legged robotics, accurate state estimation is crucial for control and planning. While traditional methods suffice for fully-actuated platforms, under-actuated systems face challenges due to sensory limitations and uncertainties. This paper presents a novel methodology for state estimation and phase prediction, integrating a torque-actuated spring-mass model with limited sensors using a multiple-hypotheses extended Kalman filter. Within this estimation framework, the optimal estimate is determined at each iteration by evaluating the likelihood functions associated with two distinct phase hypotheses, either stance or flight. We evaluate different sensor and motion model combinations, showing that our method achieves precise state and phase estimation even without advanced sensors for compliant and under-actuated platforms.
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200570687&origin=inward
https://hdl.handle.net/11511/110749
DOI
https://doi.org/10.23919/ecc64448.2024.10590929
Conference Name
2024 European Control Conference, ECC 2024
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Department of Electrical and Electronics Engineering, Conference / Seminar
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BibTeX
O. K. KARAGÖZ, A. Kılıç, and M. M. Ankaralı, “Model-Based Unified State and Phase Estimation for Torque Actuated Dissipative Spring-Mass Runner Using Limited Sensory Information,” presented at the 2024 European Control Conference, ECC 2024, Stockholm, İsveç, 2024, Accessed: 00, 2024. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200570687&origin=inward.