Abstract
In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.
Highlights
Human–robot collaborative shared environments are a topic of growing interest in industry, because of the cost reduction and productivity improvement that come with having robots and humans sharing the same workspace
When optical motion capture (OMC) systems are applied to monitoring human–robot collaborative cells, the evaluation of the metrology of the optical system often requires procedures with custommade calibrated devices
This work details the results of a methodology to evaluate the metrological performance of OMC spaces dedicated to human–robot collaborative environments, taking advantage of the performance of an industrial robot
Summary
Human–robot collaborative shared environments are a topic of growing interest in industry, because of the cost reduction and productivity improvement that come with having robots and humans sharing the same workspace In this type of production cell, it is mandatory to track the human operator’s behaviour in order to guarantee their safety [1,2,3]. OMCs are widely used in the biomedical fields [5]—for example, biomechanics [6], sports [7], ergonomics, gait analysis [8], gerontology, or rehabilitation [9] Their accuracy makes them able to be used as a gold standard for evaluating other motion measurement systems [10,11,12]. They are increasingly used in collaborative robots, because a shared workspace equipped with an OMC system can greatly improve the possibility of safe and dynamic interactions between humans and machines [13]
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