Tele-nursing robots provide a safe approach for patient-caring in quarantine areas. For effective nurse-robot collaboration, ergonomic teleoperation and intuitive interfaces with low physical and cognitive workload must be developed. We propose a framework to evaluate the control interfaces to iteratively develop an intuitive, efficient, and ergonomic teleoperation interface. The framework is a hierarchical procedure that incorporates general to specific assessment and its role in design evolution. We first present pre-defined objective and subjective metrics used to evaluate three representative contemporary teleoperation interfaces. The results indicate that teleoperation via human motion mapping outperforms the gamepad and stylus interfaces. The trade-off with using motion mapping as a teleoperation interface is the non-trivial physical fatigue. To understand the impact of heavy physical demand during motion mapping teleoperation, we propose an objective assessment of physical workload in teleoperation using electromyography (EMG). We find that physical fatigue happens in the actions that involve precise manipulation and steady posture maintenance. We further implemented teleoperation assistance in the form of shared autonomy to eliminate the fatigue-causing component in robot teleoperation via motion mapping. The experimental results show that the autonomous feature effectively reduces the physical effort while improving the efficiency and accuracy of the teleoperation interface.
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Mitigating Network Latency in Cloud-Based Teleoperation using Motion Segmentation and Synthesis
Network latency is a major problem in Cloud Robotics for
human robot interactions such as teleoperation. Routing delays can be
highly variable in a heterogeneous computing environment, imposing
challenges to reliably teleoperate a robot with a closed-loop feedback
controller. By sharing Gaussian Mixture Models (GMMs), Hidden Semi-
Markov Models (HSMMs), and linear quadratic tracking (LQT) con-
trollers between the cloud and the robot. We build a motion recognition,
segmentation, and synthesis framework for Cloud Robotic teleoperation;
and we introduce a set of latency mitigation network protocols under
this framework. We use this framework in experiments with a dynamic
robot arm to perform learned hand-written letter motions.We then study
the motion recognition errors, motion synthesis errors, and the latency
mitigation performance.
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- Award ID(s):
- 1838833
- NSF-PAR ID:
- 10204349
- Date Published:
- Journal Name:
- International Symposium on Robotics Research
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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