RETRACTED ARTICLE: Robot acquisition, control and interfacing using multimodal feedback

Ande, Stanly Kumar; Kuchibotla, Mallikarjuna Rao; Adavi, Bala Krishna

doi:10.1007/s12652-020-01738-0

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…The author of another paper (Ande, Kuchibotla, Adavi, 2020) proposes an interesting application of HMM, namely audio-visual speech recognition. In this study, visual features are combined with audio features using the early integration method followed by the classification of speech using the hidden Markov model.…”

Section: Related Workmentioning

confidence: 99%

HMM-based phoneme speech recognition system for the control and command of industrial robots

Naik

2021

Technical Transactions

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In recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (roboticarm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.

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Section: Related Workmentioning

confidence: 99%

HMM-based phoneme speech recognition system for the control and command of industrial robots

Naik

2021

Technical Transactions

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“…This can be attributed to the strong reliability and maneuverability of WRs, which enable them to outperform hand-worked specialty in terms of precision and consistency. Additionally, WRs offer the advantage of protecting workers from exposure to dirty and hazardous environments [6,7]. Nevertheless, it is important to note that the welding process is characterized by its high energy density [8], and the widespread utilization of WRs has significant environmental implications.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

Yan,

Zhang,

2023

Sustainability

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The welding process is characterized by its high energy density, making it imperative to optimize the energy consumption of welding robots without compromising the quality and efficiency of the welding process for their sustainable development. The above evaluation objectives in a particular welding situation are mostly influenced by the welding process parameters. Although numerical analysis and simulation methods have demonstrated their viability in optimizing process parameters, there are still limitations in terms of modeling accuracy and efficiency. This paper presented a framework for optimizing process parameters of welding robots in industry settings, where data augmentation was applied to expand sample size, auto machine learning theory was incorporated to quantify reflections from process parameters to evaluation objectives, and the enhanced non-dominated sorting algorithm was employed to identify an optimal solution by balancing these objectives. Additionally, an experiment using Q235 as welding plates was designed and conducted on a welding platform, and the findings indicated that the prediction accuracy on different objectives obtained by the enlarged dataset through ensembled models all exceeded 95%. It is proven that the proposed methods enabled the efficient and optimal determination of parameter instructions for welding scenarios and exhibited superior performance compared with other optimization methods in terms of model correctness, modeling efficiency, and method applicability.

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Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly

Liu

Wang

et al. 2021

Procedia CIRP

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RETRACTED ARTICLE: Robot acquisition, control and interfacing using multimodal feedback

Cited by 5 publications

References 4 publications

HMM-based phoneme speech recognition system for the control and command of industrial robots

HMM-based phoneme speech recognition system for the control and command of industrial robots

Multi-Objective Considered Process Parameter Optimization of Welding Robots Based on Small Sample Size Dataset

Leveraging multimodal data for intuitive robot control towards human-robot collaborative assembly

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