Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms
Abstract
:1. Introduction
2. Related Work
3. Control Types for a 3D Environment
3.1. Manually Designed DoF-Calculations
3.2. Classic Control Type
- X-Translation + Y-Translation;
- Z-Translation + Roll;
- Yaw + Pitch;
- Open/Close fingers + Nothing.
3.3. Double Arrow Control Type
3.4. Control Type Single Arrow
4. Materials and Methods
4.1. Hypotheses
- Average Task Completion Time
- –
- H1 Double Arrow leads to lower task completion time than Classic. The adaptive control of Double Arrow should significantly reduce the movements necessary to perform the task by combining different cardinal DoFs into one continuous movement, which otherwise would each have to be adjusted separately.
- –
- H2 Single Arrow leads to lower task completion time than Double Arrow. Only using one arrow for each DoF mapping should reduce visual clutter. This should lead to a shorter processing time of the suggested movements, reducing the total time to execute a task.
- Average Number of Mode Switches
- –
- H3 Double Arrow leads to fewer mode switches than Classic. The adaptive control of Double Arrow should reduce the necessity to switch modes significantly. Since different DoFs are combined depending on the current situation, a change in position and rotation brings the robot arm closer to the target and can be performed without mode switches.
- –
- H4 Single Arrow and Double Arrow need roughly an equal number of mode switches. The behavior of the two adaptive control types is the same. Thus, while it might take participants longer to understand what movements they can perform with Double Arrow as opposed to Single Arrow, they should switch modes approximately as often in both control types.
- Workload
- –
- H5 Double Arrow leads to lower NASA TLX scores than Classic. The adaptive control of Double Arrow calculates sensible movements to reach the next goal position and rotation. Thus, it should alleviate the participants from having to think of a sequence of movements to reach their goal, reducing workload. This is in contrast to the findings of our previous study, in which participants perceived the Adaptive control as more complex than the Standard control [6]. We expect the benefit of pre-calculated DoF combinations and the workload of developing a sequence of movements in cardinal DoFs to be higher in a 3D environment than in a 2D environment. Therefore, the workload for the adaptive control types should be lower than for Classic in 3D.
- –
- H6 Single Arrow leads to lower NASA TLX scores than Double Arrow. Since we assume that reduced visual clutter leads to a shorter processing time for the suggested movements, the NASA TLX scores of Single Arrow should be lower.
4.2. Participants
4.3. Apparatus
4.4. Procedure
4.5. Study Design
- Average Task Completion Time in seconds While participants executed each trial with the robot arm, the time to complete the task was measured for each participant. Then, the average task completion time for each control type was calculated across all participants.
- Average Number of Mode Switches While participants executed each trial with the robot arm, each mode switch executed by pressing a button on the input device was counted and stored as the number of mode switches. Then, the average number of mode switches for each control type was calculated across all participants.
- Workload via a NASA Raw-TLX questionnaire After completing all trials within each control type, the participants were asked to fill out a NASA Raw-TLX questionnaire to obtain information about the participants’ perceived workload. The questionnaire consists of the following six criteria, which participants would rate on a scale of 0 to 100 in steps of 5: mental demand, physical demand, temporal demand, performance, effort, and frustration [28].
4.6. Task
5. Results
5.1. Quantitative Results
- IQR: Interquartile Range;
- SD: Standard Deviation;
- SE: Standard Error;
- p: p-value as an expression of the level of statistical significance;
- N: Sample Size;
- (2): Chi-Squared with two degrees of freedom;
- F: F-Statistic for the Repeated-Measures ANOVA;
- M: Mean;
- df: Degrees of Freedom for the calculation of for the Friedman Tests.
5.1.1. Task Completion Time
5.1.2. Mode Switches
5.1.3. Workload and Rank
5.2. Qualitative Results
5.2.1. Thematic Analysis
5.2.2. Results of the Thematic Analysis
6. Discussion
7. Limitations
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Classic | Double Arrow | Single Arrow | |
---|---|---|---|
Mean | 47.41 | 42.62 | 44.04 |
Median | 44.66 | 37.75 | 41.23 |
Std.-Dev. | 12.55 | 19.28 | 22.24 |
IQR | 14.03 | 24.33 | 31.68 |
Classic | Double Arrow | Single Arrow | |
---|---|---|---|
Mean | 17.87 | 12.93 | 14.23 |
Median | 16.50 | 11.63 | 12.31 |
Std.-Dev. | 4.80 | 3.91 | 5.15 |
IQR | 7.00 | 5.09 | 7.91 |
Mental | Physical | Temporal | Performance | Effort | Frustration | |
---|---|---|---|---|---|---|
Demand | Demand | Demand | ||||
Classic (Mean) | 53.33 | 30.26 | 36.92 | 32.05 | 48.59 | 41.41 |
Classic (Std.-Dev.) | 24.64 | 21.67 | 21.07 | 20.48 | 24.84 | 24.52 |
Double Arrow (Mean) | 56.28 | 28.21 | 40.38 | 38.97 | 52.82 | 43.08 |
Double Arrow (Std.-Dev.) | 22.93 | 16.20 | 25.06 | 25.50 | 24.08 | 26.40 |
Single Arrow (Mean) | 48.97 | 27.56 | 36.03 | 40.64 | 51.41 | 38.33 |
Single Arrow (Std.-Dev.) | 24.69 | 22.94 | 20.56 | 26.61 | 23.25 | 26.34 |
Mean Ranks | ||||||
Classic | 2.04 | 1.92 | 1.96 | 1.73 | 1.79 | 1.92 |
Double Arrow | 2.21 | 2.17 | 2.18 | 2.15 | 2.18 | 2.17 |
Single Arrow | 1.76 | 1.91 | 1.86 | 2.12 | 2.03 | 1.91 |
Friedman Tests | ||||||
4.23 | 2.07 | 2.38 | 4.86 | 3.15 | 1.76 | |
Exact Significance | 0.12 | 0.37 | 0.31 | 0.09 | 0.21 | 0.43 |
NASA TLX | Rank | |
---|---|---|
Classic (Mean) | 40.43 | 1.87 |
Classic (Std.-Dev.) | 17.11 | 0.77 |
Double Arrow (Mean) | 43.29 | 2.05 |
Double Arrow (Std.-Dev.) | 15.32 | 0.86 |
Single Arrow (Mean) | 40.49 | 2.08 |
Single Arrow (Std.-Dev.) | 17.29 | 0.84 |
Mean Ranks | ||
Classic | 1.85 | 1.87 |
Double Arrow | 2.29 | 2.05 |
Single Arrow | 1.86 | 2.08 |
Friedman Tests | ||
5.33 | 0.97 | |
Exact Significance | 0.07 | 0.65 |
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Kronhardt, K.; Rübner, S.; Pascher, M.; Goldau, F.F.; Frese, U.; Gerken, J. Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms. Technologies 2022, 10, 30. https://doi.org/10.3390/technologies10010030
Kronhardt K, Rübner S, Pascher M, Goldau FF, Frese U, Gerken J. Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms. Technologies. 2022; 10(1):30. https://doi.org/10.3390/technologies10010030
Chicago/Turabian StyleKronhardt, Kirill, Stephan Rübner, Max Pascher, Felix Ferdinand Goldau, Udo Frese, and Jens Gerken. 2022. "Adapt or Perish? Exploring the Effectiveness of Adaptive DoF Control Interaction Methods for Assistive Robot Arms" Technologies 10, no. 1: 30. https://doi.org/10.3390/technologies10010030