Abstract
The quality of the communication channel between human-robot teammates critically influences the team’s ability to perform a task safely and effectively. In this paper, we present a nine person pilot study that investigates the effects of different degradations of that communication channel, and within three shared-autonomy paradigms that differ according to how and at what level control is partitioned between the human and the autonomy. Accordingly, the rate and granularity of the human input differs for each shared-autonomy paradigm. We refer to each paradigm according to the input expected from the user, namely high-level, mid-level and low-level control paradigms. We find three primary insights. First, interruptions in the signal transmission (dropped signals) decrease safety and performance in modes where continuous and high-bandwidth inputs from the human are expected. Second, decreased transmission frequency offers a trade-off between safety and performance for low-level and mid-level control paradigms. Lastly, noise alters the safety of high-level input since the user is not continually correcting the signal. These insights inform us when to shift autonomy levels depending on the quality of the communication channel, which can vary with time. Knowing the ground truth of how the signal was degraded, we evaluate a recurrent neural network’s ability to classify whether the communication channel is experiencing lowered transmission frequency, dropped signals or noise, and we find an accuracy of 90% when operating with low-level commands. Combined with the key insights, our results indicate that a framework to dynamically allocate autonomy between the user and robot could improve overall performance.
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Notes
- 1.
Note the primary differences between \(C_L\) and \(C_M\) are the discrete input and the rate of input.
- 2.
RVIZ is a 3D visualization tool distributed with the Robot Operating System (ROS).
- 3.
We have the subjects stand at a static operator station, instead of riding the wheelchair, in order to allow for the assessment of subject attention using an established distraction task [12] that is well-studied within the human factors literature. This task requires the subject to monitor a screen and interact with a keyboard, which was an overly cumbersome setup to have onboard the wheelchair.
- 4.
For this study, we use the following System Monitoring Subtask Basic Parameters: (a) Gauge Speed Lower Limit \(=2\), (b) Gauge Speed Upper Limit \(=4\), (c) Correct Fault Identification Pause \(=10\) and (d) Gauge Malfunction Timeout \(=10\). We use the keyboard as the only input option.
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Acknowledgements
This work was supported by grant from U.S. Office of Naval Research under the Award Number N00014-16-1-2247, which we gratefully acknowledge. The authors would also like to thank Enid Montague for her guidance with the distraction task.
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Young, M., Nejati, M., Erdogan, A., Argall, B. (2018). An Analysis of Degraded Communication Channels in Human-Robot Teaming and Implications for Dynamic Autonomy Allocation. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_43
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DOI: https://doi.org/10.1007/978-3-319-67361-5_43
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