DeltaCharger: Charging Robot With Inverted Delta Mechanism and CNN-Driven High Fidelity Tactile Perception for Precise 3D Positioning

Okunevich, Iaroslav; Trinitatova, Daria; Kopanev, Pavel; Tsetserukou, Dzmitry

doi:10.1109/lra.2021.3098838

Cited by 11 publications

(3 citation statements)

References 20 publications

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“…Sundaram et al (2019) use CNN to capture the embedding of each frame and simply concatenate them for action classification. Recent studies enhance this method by adopting a GRU/LSTM model rather than concatenation to model the sequential features (Luo et al 2021;Okunevich et al 2021;Gao et al 2020). Cao et al (2020) introduces temporal attention operation combined with spatial features in separate phases.…”

Section: Related Work Action Classification With Tactile Signalsmentioning

confidence: 99%

“…Similar to visual and acoustic signals, tactile signals are important for modeling and understanding humans. In recent years, various wearable electronics have been designed to collect tactile signals, which are widely used in multiple scenarios, especially in healthcare and robotics (Zhu et al 2019;Fan et al 2020;Lou et al 2020;Okunevich et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification

Lin,

Li,

Gao

et al. 2024

AAAI

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Tactile signals collected by wearable electronics are essential in modeling and understanding human behavior. One of the main applications of tactile signals is action classification, especially in healthcare and robotics. However, existing tactile classification methods fail to capture the spatial and temporal features of tactile signals simultaneously, which results in sub-optimal performances. In this paper, we design Spatio-Temporal Aware tactility Transformer (STAT) to utilize continuous tactile signals for action classification. We propose spatial and temporal embeddings along with a new temporal pretraining task in our model, which aims to enhance the transformer in modeling the spatio-temporal features of tactile signals. Specially, the designed temporal pretraining task is to differentiate the time order of tubelet inputs to model the temporal properties explicitly. Experimental results on a public action classification dataset demonstrate that our model outperforms state-of-the-art methods in all metrics.

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Section: Related Work Action Classification With Tactile Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification

Lin,

Li,

Gao

et al. 2024

AAAI

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“…The Covid-19 pandemic of 2020 has created a rapidly growing trend for minimizing personal contact and robotic automatization in all areas of human life and industry application, e.g., for stocktaking automation in warehouses [4]- [7], shopping malls [8], disinfection of hospitals and offices [9], [10], and autonomous chargers [11]. For epidemiological security, and due to vast advances in automation technology, various contact-less last-mile delivery methods are currently being developed.…”

Section: A Motivationmentioning

confidence: 99%

CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

Protasov¹,

Karpyshev²,

Kalinov³

et al. 2021

Preprint

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Mobile autonomous robots include numerous sensors for environment perception. Cameras are an essential tool for robot's localization, navigation, and obstacle avoidance. To process a large flow of data from the sensors, it is necessary to optimize algorithms, or to utilize substantial computational power. In our work, we propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous outdoor mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360degree field of view was used as the experimental setup. The obtained experimental results revealed that the proposed optimization accelerates the inference time of the neural network in the cases with up to 5 out of 6 cameras containing target objects.

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A review of robotic charging for electric vehicles

Saputra,

Nor,

Rijanto

et al. 2023

Int J Intell Robot Appl

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DeltaCharger: Charging Robot With Inverted Delta Mechanism and CNN-Driven High Fidelity Tactile Perception for Precise 3D Positioning

Cited by 11 publications

References 20 publications

Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification

Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification

CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification

A review of robotic charging for electric vehicles

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