Exploring surface detection for a quadruped robot in households

Kertész, Csaba

doi:10.1109/icarsc.2014.6849778

Cited by 5 publications

(17 citation statements)

References 18 publications

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“…Two classifiers (support vector machine (SVM), naïve Bayes) were cross-validated and the performance was estimated at 96.3% with four surfaces (plastic foil, cardboard, Styrofoam and rubber). This result can be compared to the previous work of the author [10] where 93% accuracy was estimated with 10-fold CV when a Sony ERS-7 walked on five surfaces (wood, short carpet, carpet, foam mats, vinyl).…”

Section: Introduction and Related Workmentioning

confidence: 71%

“…A Sony ERS-7 walked with singular crawling during the experiments to protect the weak servomotors and maintain the stability by having three legs always on the ground [10]. The walk period (φ) was 2400 msec (2.4 Hz) and the speed was around 2cm/sec.…”

Section: A Gait and Surfacesmentioning

confidence: 99%

“…Unlike when the robots walked on one example per domestic surface type in [5,8,9,10,13,15], AIBO run on multiple examples per type to gather samples in a more generic manner in this paper. The intraclass variability in the dataset were higher in this way and the interclass correlation had a higher chance tough these properties were not studied.…”

Section: A Gait and Surfacesmentioning

confidence: 99%

“…Various locomotion options and sensor combinations have been examined in the past works: wheeled [11,12,16,17], quadruped [3,4,8,9,10,13], six-legged robots [15]. Although some studies analyzed a few sensors [9,11] or the body oscillations [18], this paper does not go into this direction, but the focus is on the classifier model building and the evaluation to provide a better interpretation of the past results from machine learning point of view.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Although some studies analyzed a few sensors [9,11] or the body oscillations [18], this paper does not go into this direction, but the focus is on the classifier model building and the evaluation to provide a better interpretation of the past results from machine learning point of view. Most reviewed studies evaluated their models properly [1,3,8,9,10,12,13], but some did not employed (cross-)validation to get a better estimate of the real accuracy [6,11,15]. This paper uses a four-legged Sony ERS-7 robot and the feature vector is composed by the readings of four sensors to train the models.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Rigidity-Based Surface Recognition for a Domestic Legged Robot

Kertész¹

2016

IEEE Robot. Autom. Lett.

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Abstract-Although the infrared (IR) range and motor force sensors have been rarely applied to the surface recognition of mobile robots, they are fused in this paper with accelerometer and ground contact force sensors to distinguish six indoor surface types. Their sensor values are affected by the crawling gait period, therefore, certain components of the fast Fourier transform over these data are included in the feature vectors as well as remarkable discriminative power is observed for the same scalar statistics of different sensing modalities. The machine learning aspects are analyzed with random forests (RF) because of their stable performance and some inherent, beneficial properties for the model development process. The robustness is evaluated with unseen data after the model accuracy is estimated with cross-validation (CV), and regardless whether a Sony ERS-7 walks barefoot or wears socks, the forests achieve 94% accuracy. This result outperforms the state of the art techniques for indoor surfaces in the literature and the classification execution is real-time on the robot. The above mentioned model development process with RF is documented to create new models for other robots more quickly and efficiently.

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Section: Introduction and Related Workmentioning

confidence: 71%

Section: A Gait and Surfacesmentioning

confidence: 99%

Section: A Gait and Surfacesmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

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Rigidity-Based Surface Recognition for a Domestic Legged Robot

Kertész¹

2016

IEEE Robot. Autom. Lett.

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Fast, accurate and explainable time series classification through randomization

Cabello,

Naghizade,

et al. 2023

Data Min Knowl Disc

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Time series classification (TSC) aims to predict the class label of a given time series, which is critical to a rich set of application areas such as economics and medicine. State-of-the-art TSC methods have mostly focused on classification accuracy, without considering classification speed. However, efficiency is important for big data analysis. Datasets with a large training size or long series challenge the use of the current highly accurate methods, because they are usually computationally expensive. Similarly, classification explainability, which is an important property required by modern big data applications such as appliance modeling and legislation such as the European General Data Protection Regulation, has received little attention. To address these gaps, we propose a novel TSC method – the Randomized-Supervised Time Series Forest (r-STSF). r-STSF is extremely fast and achieves state-of-the-art classification accuracy. It is an efficient interval-based approach that classifies time series according to aggregate values of the discriminatory sub-series (intervals). To achieve state-of-the-art accuracy, r-STSF builds an ensemble of randomized trees using the discriminatory sub-series. It uses four time series representations, nine aggregation functions and a supervised binary-inspired search combined with a feature ranking metric to identify highly discriminatory sub-series. The discriminatory sub-series enable explainable classifications. Experiments on extensive datasets show that r-STSF achieves state-of-the-art accuracy while being orders of magnitude faster than most existing TSC methods and enabling for explanations on the classifier decision.

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