Mechanical Design and Kinematic Control of a Humanoid Robot Face

Qiu, Yu; Xu, Chang; Su, Manjia; Chen, Hongkai; Guan, Yisheng; Zhu, Haifei

doi:10.1007/978-3-030-27526-6_3

Cited by 1 publication

(1 citation statement)

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“…Software designed for simulating the interactive humanmachine scenarios, such as SAMNIE CAD, JACK, etc, have been applied to analyse driver's visual field and assess the visible and invisible areas (Wu et al 2018,Qiu et al 2019. These software provides a monocular view, and the views from the left and right eyes can be assessed to simulate binocular vision.…”

Section: Improvements Of the Modelmentioning

confidence: 99%

Prediction of the distribution of tractor driver’s eye positions

Huang¹,

Chen²,

Qiu³

et al. 2023

Meas. Sci. Technol.

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A predictive model of tractor driver’s eye positions based on the anthropometric parameters and tractor cab layout parameters was established using the statistical approach. To our knowledge, this model is the first published predictive model of tractor drivers’ eye positions that takes account of their body dimensions and tractor types. In order to predict the distribution of the drivers’ eye positions precisely, the fore-and-aft positions of the driving seat determined by 180 tractor drivers in three different tractors and the relative positions of the eyes to the designated reference points were recorded to quantitatively demonstrate the distribution rule of their eye positions. The multiple linear regression and principal component analysis were adopted to processing the feature parameters of human and tractor, and the prediction effect of the model was assessed according to the proportion of the driver’s eye positions from side view those were within the range of the eyellipse model. The experimental result revealed that the centroid positions and principal axis dip angle of the model differed from each other respectively, while the axis lengths were the same. The principal axis dip angle was horizontal or oblique forward-and-upward. And the tractor layout parameters had a significant impact on the centroid coordinate and principal axis dip angle of the model, while the dimension of the model was associated with the anthropometric parameters mainly. The prediction accuracy of the model exceeded 80%. The regression predictive model provides a reference tool for the visual field design of tractor displays and cab layouts.

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Section: Improvements Of the Modelmentioning

confidence: 99%