An attempt to relate dynamic tracking error to occurring situation based on additional rectilinear motion for five-axis machine tools

Ding, Qicheng; Ding, Jiexiong; Zhang, Jing; Du, Lan

doi:10.1177/1687814020967573

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Due to the lack of standardized evaluation techniques for dynamic hand movements [besides methods for machine tools and iGPS and laser tracker (Wang et al, 2011;Ding et al, 2020)], we present our own method, tailored to our experiment, to determine the tracking accuracy and error curve for dynamic hand movements.…”

Section: Evaluating Hand Tracking Accuracymentioning

confidence: 99%

Evaluation and improvement of HMD-based and RGB-based hand tracking solutions in VR

Reimer

Podkosova

Scherzer

et al. 2023

Front. Virtual Real.

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Hand tracking has become a state-of-the-art technology in the modern generation of consumer VR devices. However, off-the-shelf solutions do not support hand detection for more than two hands at the same time at distances beyond arm’s length. The possibility to track multiple hands at larger distances would be beneficial for colocated multi-user VR scenarios, allowing user-worn devices to track the hands of other users and therefore reducing motion artifacts caused by hand tracking loss. With the global focus of enabling natural hand interactions in colocated multi-user VR, we propose an RGB image input-based hand tracking method, built upon the MediaPipe framework, that can track multiple hands at once at distances of up to 3 m. We compared our method’s accuracy to that of Oculus Quest and Leap Motion, at different distances from the tracking device and in static and dynamic settings. The results of our evaluation show that our method provides only slightly less accurate results than Oculus Quest or Leap motion in the near range (with median errors below 1.75 cm at distances below 75 cm); at larger distances, its accuracy remains stable (with a median error of 4.7 cm at the distance of 2.75 m) while Leap Motion and Oculus Quest either loose tracking or produce very inaccurate results. Taking into account the broad choice of suitable hardware (any RGB camera) and the ease of setup, our method can be directly applied to colocated multi-user VR scenarios.

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Section: Evaluating Hand Tracking Accuracymentioning

confidence: 99%

Evaluation and improvement of HMD-based and RGB-based hand tracking solutions in VR

Reimer

Podkosova

Scherzer

et al. 2023

Front. Virtual Real.

View full text Add to dashboard Cite

show abstract

“…Flohic et al analysed the influence of SDMD on dual ball bar test results, identified the servo parameters that affect SDMD, and optimized the dynamic performance of the machine tool by adjusting the identified servo parameters [18]. Jiang et al identified the factors that influence the SDMD based on the results of a rotation tool centre point (RTCP) test, which is one of the most effective experimental methods for testing the dynamic performance of five-axis machine tools [19,20], and achieved preliminary tracing of the machine axis system to be optimized without considering other error factors [21]. Ding et al optimized the RTCP test process to improve the identifiability of tracking errors based on their influence on SDMD [22].…”

Section: Introductionmentioning

confidence: 99%

Dynamic performance optimization of five-axis machine tools based on S-specimen and servo dynamics matching degree indexes

Guan,

Chen,

Wang

2024

Preprint

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With the advancement of the manufacturing industry, there is a growing need for high-speed five-axis machine tools that offer superior precision. Current research indicates that with the increase in the feed rate of machine tools, the influence of machine tool dynamic performance on precision becomes increasingly significant. In addition to the tracking performance of a single machine axis system, the servo dynamics matching degree (SDMD) between each machine axis system has also been widely recognized as one of the important factors affecting the dynamic performance in recent research. As an ISO standard specimen, the S-specimen has emerged as the frequently utilized specimens for machine tool acceptance testing, owing to its capacity to better reflect the dynamic performance of five-axis machine tools. Nevertheless, the intricate mapping relationship between the factors influencing SDMD and the contour error of the S-specimen constrains the applicability of this standard specimen in error diagnosis and subsequent dynamic performance optimization. Therefore, based on the machining process of the S-specimen, a dynamic performance optimization method by improving SDMD was proposed in this paper. An index of SDMD between two machine axis systems (two-axis SDMDI) was established which clarifies the specific source for error diagnosis. A calculation model of the two-axis SDMDIs of five-axis machine tool was established based on the mapping relationship between the index and the contour error of S-specimen which provided an effective way for optimization of SDMD based on S-specimen machining test. Experiments were carried out to verify the effectiveness of the proposed optimization method.

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RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm

Ding

Wang

Ding

et al. 2021

Int J Adv Manuf Technol

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An attempt to relate dynamic tracking error to occurring situation based on additional rectilinear motion for five-axis machine tools

Cited by 4 publications

References 24 publications

Evaluation and improvement of HMD-based and RGB-based hand tracking solutions in VR

Evaluation and improvement of HMD-based and RGB-based hand tracking solutions in VR

Dynamic performance optimization of five-axis machine tools based on S-specimen and servo dynamics matching degree indexes

RTCP test axis motion planning for five-axis machine tool dynamic performance using observability optimization based on modified genetic algorithm

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