Abstract
Highly precise vehicle dynamics modeling is indispensable for self-driving technology. We propose a model learning framework, which utilizes FAD (The abbreviation of the capital letters of free dynamics, actuator, and disturbance.) learning, motor babbling, and dynamics learning tree. In the proposed framework, modeling error was decreased compared with conventional neural network approach. Also, this framework is applicable to online learning. In experiments, FAD learning and dynamics learning tree decreased learning error. The dynamics of a simulated car was learned using motor babbling. The proposed framework is applicable to a variety of mechanical systems.
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Notes
- 1.
The same algorithm is used in another proposition [25] that is presented in this conference.
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A part of this work was supported by JSPS KAKENHI Grant Number 15K20850.
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Eto, K., Kobayashi, Y., Kim, C.H. (2016). Vehicle Dynamics Modeling Using FAD Learning. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_65
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