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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

Illustration of human-aware motion planning. The left panel depicts a shared workspace in which a human and robot are placing and sealing screws, respectively. The right panel depicts both the standard, shortest-path motion (dashed arrow) and a human-aware motion (solid arrow) that the robot could take given the expected human workspace occupancy, represented by the cylinder.
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fig1-0018720814565188: Illustration of human-aware motion planning. The left panel depicts a shared workspace in which a human and robot are placing and sealing screws, respectively. The right panel depicts both the standard, shortest-path motion (dashed arrow) and a human-aware motion (solid arrow) that the robot could take given the expected human workspace occupancy, represented by the cylinder.

Mentions: To illustrate this technique, consider the shared workspace depicted in Figure 1. The left side of the figure indicates a shared workspace in which a human and robot place screws and apply a sealant, respectively. If we can accurately predict that the human worker will place a screw at the third hole from the left, beside the two screws already placed, we can then approximate the portion of the shared workspace that the human worker will use within the next several moments. A simple and effective motion model for this particular task is the approximation of the predicted workspace occupancy of the human via a cylinder that encompasses the worker’s arm as he or she completes the placing task, as this is the only part of the human the robot can reach. This cylinder is indicated in the virtual representation of the workspace depicted on the right side of Figure 1. Once the robot has made a prediction of the workspace occupancy of the human, it can adapt its motion planning by selecting a path to its own goal—in this case, the second screw from the left—that avoids the area that the model predicts the human will occupy. This human-aware motion is depicted in the figure as a solid arrow, and the simple, shortest-path motion the robot would have otherwise taken is shown as a dashed arrow.


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

Lasota PA, Shah JA - Hum Factors (2015)

Illustration of human-aware motion planning. The left panel depicts a shared workspace in which a human and robot are placing and sealing screws, respectively. The right panel depicts both the standard, shortest-path motion (dashed arrow) and a human-aware motion (solid arrow) that the robot could take given the expected human workspace occupancy, represented by the cylinder.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1-0018720814565188: Illustration of human-aware motion planning. The left panel depicts a shared workspace in which a human and robot are placing and sealing screws, respectively. The right panel depicts both the standard, shortest-path motion (dashed arrow) and a human-aware motion (solid arrow) that the robot could take given the expected human workspace occupancy, represented by the cylinder.
Mentions: To illustrate this technique, consider the shared workspace depicted in Figure 1. The left side of the figure indicates a shared workspace in which a human and robot place screws and apply a sealant, respectively. If we can accurately predict that the human worker will place a screw at the third hole from the left, beside the two screws already placed, we can then approximate the portion of the shared workspace that the human worker will use within the next several moments. A simple and effective motion model for this particular task is the approximation of the predicted workspace occupancy of the human via a cylinder that encompasses the worker’s arm as he or she completes the placing task, as this is the only part of the human the robot can reach. This cylinder is indicated in the virtual representation of the workspace depicted on the right side of Figure 1. Once the robot has made a prediction of the workspace occupancy of the human, it can adapt its motion planning by selecting a path to its own goal—in this case, the second screw from the left—that avoids the area that the model predicts the human will occupy. This human-aware motion is depicted in the figure as a solid arrow, and the simple, shortest-path motion the robot would have otherwise taken is shown as a dashed arrow.

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