Probabilistic modeling of reaction force/torque through Fourier transform and entropy analysis
- Title
- Probabilistic modeling of reaction force/torque through Fourier transform and entropy analysis
- Author
- 서일홍
- Keywords
- Entropy Analysis; Fast Fourier Transform; Hidden Markov models; Recognition; Feature Selection
- Issue Date
- 2019-05
- Publisher
- IEEE
- Citation
- International Conference on Electronics, Information, and Communication, no. 8706462
- Abstract
- In this paper, we propose a method to improve the recognition performance of a probabilistic model through entropy analysis after transforming time-varying reaction force/torque signals into frequency components. To perform tasks that require physical interaction, it is important for robots to recognize reaction force/torque during the interactions between robots and environments. However, the reaction force/torque measured by F/T sensor contains a lot of noise signals due to the sensitivity of the sensor. Therefore, the recognition performance depends on noise signals included in training and/or test dataset. To solve this problem, the reaction force/torque signals are transformed from time domain into frequency domain by fast Fourier transform. Then, some task-relevant frequency components are selected based on their entropy analysis, after which they are used to learn a hidden Markov model. To evaluate our proposed method, several robot manipulation tasks are performed using an open dataset including reaction force/torque signals: approaching, transferring and positioning.
- URI
- https://ieeexplore.ieee.org/document/8706462https://repository.hanyang.ac.kr/handle/20.500.11754/110996
- ISBN
- 978-899500444-9
- DOI
- 10.23919/ELINFOCOM.2019.8706462
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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