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Effect of Automation Level on Cognitive Workload when Collaborating with a Robotic Assistant.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Effect of Automation Level on Cognitive Workload when Collaborating with a Robotic Assistant./
作者:	Champagne, Mitchell A.
面頁冊數:	1 online resource (48 pages)
附註:	Source: Masters Abstracts International, Volume: 84-03.
Contained By:	Masters Abstracts International84-03.
標題:	Collaboration. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29281526click for full text (PQDT)
ISBN:	9798845434456

Effect of Automation Level on Cognitive Workload when Collaborating with a Robotic Assistant.
Champagne, Mitchell A.

Effect of Automation Level on Cognitive Workload when Collaborating with a Robotic Assistant. - 1 online resource (48 pages)

Source: Masters Abstracts International, Volume: 84-03.

Thesis (M.Sc.)--Louisiana State University and Agricultural & Mechanical College, 2022.

Includes bibliographical references

Manufacturing robotics have been used for decades to perform repetitive tasks, or tasks that require increased levels of speed, strength, or precision to meet production and specification requirements. Determining the appropriate degree of automation for both the human and robot collaborative team members is critical to optimize production as well as user experience. If the degree of automation is too high, the human will be out of the loop which can result in the loss of situational awareness and be detrimental to intervention time and accuracy. If the degree of automation is too low, then the human may experience greater than necessary cognitive workloads resulting in fatigue.In this experiment, the human team member performed a typing task while the robot team member performed a pick and place task. The human remained in a supervisory role to the robot's actions. As the degree of automation increased, objective and subjective measurements of the cognitive effect were collected. 30 participants aged 18-44 years were divided between two levels of automation. Group one (n=15) participated in the decision support variation of the experiment. The human team member typed from provided literature while they supervised the Universal Robot performing a pick and place task. The robot then initiated movement to pick up the object when the human pressed a button. Group two participated in the automatic execution variation of the experiment in which the robot executed all movements automatically, without input from the human operator. The robot performed the pick task when an object was detected by a photelectric switch. In each scenario, an error was presented on the 5th, 9th, 15th, and 19th production cycle requiring the human to intervene. Time measurements were collected at this juncture to determine how the human reacted to an unexpected situation at each level of automation.The Subjective Workload Assessment Technique (SWAT) (Luximon&Goonetilleke, 2001) was used to measure the perceived cognitive workload for each participant. Time data were collected for the production cycle and intervention. Typed words per minute were collected and compared to each participant's control. T-tests were used to analyze the mean time within groups comparing the control words per minute to the treatment words per minute. Additionally, t-tests were used to analyze mean time between groups in the following areas, time to complete twenty cycles, intervention time, and words per minute.Two hypotheses were developed which results were measured against. The first hypothesis is that the automatic execution operators will be out of the loop while engrossed in the typing task and take more time to notice the error, understand the problem, correct the problem, and return the system to homeostasis than decision support operators. The experiment results did not fully support this hypothesis, but they did reveal that the increased level of automation completed 20 cycles significantly (p < 0.0001) faster mean time 9.57 min (SD 0.2 x 10-3) than the lower level of automation with a mean time 10.25 min SD 0.2 x 10-4.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798845434456Subjects--Topical Terms:

3556296
Collaboration.
Index Terms--Genre/Form:

542853
Electronic books.

Effect of Automation Level on Cognitive Workload when Collaborating with a Robotic Assistant.
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520 $a Manufacturing robotics have been used for decades to perform repetitive tasks, or tasks that require increased levels of speed, strength, or precision to meet production and specification requirements. Determining the appropriate degree of automation for both the human and robot collaborative team members is critical to optimize production as well as user experience. If the degree of automation is too high, the human will be out of the loop which can result in the loss of situational awareness and be detrimental to intervention time and accuracy. If the degree of automation is too low, then the human may experience greater than necessary cognitive workloads resulting in fatigue.In this experiment, the human team member performed a typing task while the robot team member performed a pick and place task. The human remained in a supervisory role to the robot's actions. As the degree of automation increased, objective and subjective measurements of the cognitive effect were collected. 30 participants aged 18-44 years were divided between two levels of automation. Group one (n=15) participated in the decision support variation of the experiment. The human team member typed from provided literature while they supervised the Universal Robot performing a pick and place task. The robot then initiated movement to pick up the object when the human pressed a button. Group two participated in the automatic execution variation of the experiment in which the robot executed all movements automatically, without input from the human operator. The robot performed the pick task when an object was detected by a photelectric switch. In each scenario, an error was presented on the 5th, 9th, 15th, and 19th production cycle requiring the human to intervene. Time measurements were collected at this juncture to determine how the human reacted to an unexpected situation at each level of automation.The Subjective Workload Assessment Technique (SWAT) (Luximon&Goonetilleke, 2001) was used to measure the perceived cognitive workload for each participant. Time data were collected for the production cycle and intervention. Typed words per minute were collected and compared to each participant's control. T-tests were used to analyze the mean time within groups comparing the control words per minute to the treatment words per minute. Additionally, t-tests were used to analyze mean time between groups in the following areas, time to complete twenty cycles, intervention time, and words per minute.Two hypotheses were developed which results were measured against. The first hypothesis is that the automatic execution operators will be out of the loop while engrossed in the typing task and take more time to notice the error, understand the problem, correct the problem, and return the system to homeostasis than decision support operators. The experiment results did not fully support this hypothesis, but they did reveal that the increased level of automation completed 20 cycles significantly (p < 0.0001) faster mean time 9.57 min (SD 0.2 x 10-3) than the lower level of automation with a mean time 10.25 min SD 0.2 x 10-4.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
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650 4 $a Communication. $3 524709
650 4 $a Intervention. $3 3435307
650 4 $a Optimization. $3 891104
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650 4 $a Accident prevention. $3 3683903
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650 4 $a Musculoskeletal diseases. $3 3548329
650 4 $a Human performance. $3 3562051
650 4 $a Teams. $3 3546566
650 4 $a Word processing. $3 564172
650 4 $a Personal computers. $3 3561037
650 4 $a Decision making. $3 517204
650 4 $a Sustainability. $3 1029978
650 4 $a Roles. $3 3559999
650 4 $a Computer science. $3 523869
650 4 $a Industrial engineering. $3 526216
650 4 $a Physiology. $3 518431
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690 $a 0640
690 $a 0459
690 $a 0984
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710 2 $a Louisiana State University and Agricultural & Mechanical College. $3 783779
773 0 $t Masters Abstracts International $g 84-03.
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