ArticleCommunications engineering2025
A multi-method framework for establishing an angular acceleration reference in sensor calibration and uncertainty quantification.
Article in Communications engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Machine learning based real-time assessment of fabrication deviation induced mechanical performance variations in stretchable silicon arrays.Microsystems & nanoengineering · 2026Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Robots are increasingly being used across various sectors, from industry and healthcare to household applications. In practice, a pivotal challenge is the reaction to unexpected external disturbances, whose real-time feedback often relies on (noisy) sensor measurements. Subsequent inverse-dynamics calculations demand noise-amplifying numerical differentiation, leading to impracticable results. Although much effort has been spent on establishing direct measurement approaches, their measurement uncertainty quantification has not or yet insufficiently been tackled in the literature. Here, we propose a multi-method framework to develop an angular acceleration reference and provide evidence that it can serve as a measurement standard to calibrate various kinematic sensors. Within the framework, we use Monte-Carlo simulations to quantify the uncertainty of a direct measurement sensor recently developed by our team; the inertial measurement cluster (IMC). For angular accelerations up to 21 rad/s
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Registered trials
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