Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot

Luo, Ancheng; Bhattacharya, Sandip; Dutta, S.; Ochi, Yoshihiro; Miura-Mattausch, Mitiko; Weng, Jian; Zhou, Yicong; Mattausch, Hans Jürgen

doi:10.1109/jsen.2021.3059099

Cited by 9 publications

(2 citation statements)

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“…Learning/classification methods [24][25][26][27][28][29][30][31] Fast, handles missing data, accurate prediction, interpretable…”

Section: Methods Advantages Limitationsmentioning

confidence: 99%

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Ong,

Hayat,

Gomez

et al. 2024

Mathematics

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This paper presents a comprehensive study on fall recognition and forecasting for reconfigurable stair-accessing robots by leveraging deep learning techniques. The proposed framework integrates machine learning algorithms and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for fall detection of service robots on staircases. The reconfigurable stair-accessing robot sTetro serves as the platform, and the fall data required for training models are generated in a simulation environment. The two machine learning algorithms are compared and their effectiveness on the fall recognition task is reported. The results indicate that the BiLSTM model effectively classifies falls with a median categorical accuracy of 94.10% in simulation and 90.02% with limited experiments. Additionally, the BiLSTM model can be used for forecasting, which is practically valuable for making decisions well before the onset of a free fall. This study contributes insights into the design and implementation of fall detection systems for service robots used to navigate staircases through deep learning approaches. Our experimental and simulation data, along with the simulation steps, are available for reference and analysis via the shared link.

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“…Learning/classification methods [24][25][26][27][28][29][30][31] Fast, handles missing data, accurate prediction, interpretable…”

Section: Methods Advantages Limitationsmentioning

confidence: 99%

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Ong,

Hayat,

Gomez

et al. 2024

Mathematics

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“…Stability Criterion-Based Gait: Stability criterion-based approaches involve generating gait trajectories based on stability criteria such as ZMP (Zero Moment Point) trajectories. These approaches aim to ensure stability and balance during walking by controlling the robot's center of gravity and foot-ground interactions [18].…”

mentioning

confidence: 99%

Control and Gait Generation of Biped Robots: A Review

Memar Kocheh Bagh,

Liu

2024

Advances in Intelligent Manufacturing and Robotics

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This review paper explores the translation of human negotiation behaviours to bipedal walking robots, considering the inherent differences in morphology between humans and robots. Despite considerable advancements in bipedal robotics in recent years, mechanical bipeds still lack the agility and robustness exhibited by their biological counterparts, particularly in unfamiliar or dynamically changing environments. This limitation can be attributed to the absence of online motion planning, reflex-like control, and non-advantageous passive dynamics, which biological systems employ to effectively overcome disturbances, especially when encountering significant unplanned changes in stepping height.

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