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Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration.

Lasota PA, Shah JA - Hum Factors (2015)

Bottom Line: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction.Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

View Article: PubMed Central - PubMed

ABSTRACT

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.

Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human-robot interaction.

Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires.

Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot.

Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction.

Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

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Related in: MedlinePlus

Mean values, with error bars indicating standard error of the mean (SEM), of (a) task execution time, (b) percentage of concurrent motion, (c) average separation distance between the human and robot, (d) robot idle time, and (e) human idle time for the standard and human-aware robot executions.
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fig4-0018720814565188: Mean values, with error bars indicating standard error of the mean (SEM), of (a) task execution time, (b) percentage of concurrent motion, (c) average separation distance between the human and robot, (d) robot idle time, and (e) human idle time for the standard and human-aware robot executions.

Mentions: When we compared the participants’ performance while working with a human-aware robot to their performance working with a robot using standard motion planning, significant differences were found for all quantitative team fluency metrics. The Shapiro-Wilk test (at α = .05) was used to assess the normality of the data. With the exception of human idle time, W(20) = 0.817, p = .002, the distributions of the differences between the human-aware and standard task executions were not significantly different from normal, minimum W(20) = 0.943, p = .274. One-way repeated-measures ANOVAs were used where appropriate. Human idle time was instead analyzed with the Wilcoxon signed-rank test. The results of the statistical tests revealed that when working with a human-aware robot, participants completed the task 5.57% faster, F(1, 19) = 4.95, p = .038, with 19.9% more concurrent motion, F(1, 19) = 53.82, p < .001; 2.96% less human idle time, Z = −2.48, p = .013; 17.3% less robot idle time, F(1, 19) = 54.79, p < .001; and a 15.1% larger separation distance, F(1, 19) = 61.18, p < .001. The mean values for each of these metrics, along with error bars depicting standard error of the mean, are depicted in Figure 4 for both robot modes.


Analyzing the effects of human-aware motion planning on close-proximity human-robot collaboration.

Lasota PA, Shah JA - Hum Factors (2015)

Mean values, with error bars indicating standard error of the mean (SEM), of (a) task execution time, (b) percentage of concurrent motion, (c) average separation distance between the human and robot, (d) robot idle time, and (e) human idle time for the standard and human-aware robot executions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4359211&req=5

fig4-0018720814565188: Mean values, with error bars indicating standard error of the mean (SEM), of (a) task execution time, (b) percentage of concurrent motion, (c) average separation distance between the human and robot, (d) robot idle time, and (e) human idle time for the standard and human-aware robot executions.
Mentions: When we compared the participants’ performance while working with a human-aware robot to their performance working with a robot using standard motion planning, significant differences were found for all quantitative team fluency metrics. The Shapiro-Wilk test (at α = .05) was used to assess the normality of the data. With the exception of human idle time, W(20) = 0.817, p = .002, the distributions of the differences between the human-aware and standard task executions were not significantly different from normal, minimum W(20) = 0.943, p = .274. One-way repeated-measures ANOVAs were used where appropriate. Human idle time was instead analyzed with the Wilcoxon signed-rank test. The results of the statistical tests revealed that when working with a human-aware robot, participants completed the task 5.57% faster, F(1, 19) = 4.95, p = .038, with 19.9% more concurrent motion, F(1, 19) = 53.82, p < .001; 2.96% less human idle time, Z = −2.48, p = .013; 17.3% less robot idle time, F(1, 19) = 54.79, p < .001; and a 15.1% larger separation distance, F(1, 19) = 61.18, p < .001. The mean values for each of these metrics, along with error bars depicting standard error of the mean, are depicted in Figure 4 for both robot modes.

Bottom Line: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction.Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

View Article: PubMed Central - PubMed

ABSTRACT

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort.

Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human-robot interaction.

Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires.

Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot.

Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human-robot team fluency and human worker satisfaction.

Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human-robot collaboration.

Show MeSH
Related in: MedlinePlus