Developing force feedback for human-robot interactions with telerobotic manipulators
Bibliographic record
Abstract
Robotic manipulators are being used for a growing number of applications. However, one concern with robotic manipulators is that they have a limited awareness of its environment. This can cause issues with safety or control of the manipulator. Therefore, it is important to develop sensor systems for different applications. This research is part of the Musculoskeletal Tele-robotic Imaging Machine (MSK-TIM) project at the University of Saskatchewan. The purpose of this project is to develop a remotely controlled robotic manipulator to conduct medical ultrasound examinations. In human-robot interactions, safety of the human is paramount. Therefore, this research investigates two different methods to measure the contact load: a load cell and a material model. To investigate using a load cell in a teleoperated system, a 1 degree-of-freedom (DOF) force sensor was installed, and remote control of the manipulator was developed. Other upgrades include developing a graphical user interface (GUI) and supporting remote control of ultrasound parameters. Using this system, an experienced radiologist conducted remote examination of 24 arms. Afterward, both the radiologist and participants reported their experience. The changes to the MSK-TIM were found to improve the function of the device, especially image quality. The visual force feedback was noted by the radiologist to be a useful tool as it indicated when the applied force exceeded recommended limits. To investigate the material-model method of obtain force-feedback, a finite element model of the arm-probe interaction was generated. The geometry of the soft tissue was obtained using photogrammetry. Experimental stiffness behaviour of the wrist was captured using the MSK-TIM. This data was used in model-correction analysis to obtain the first-order Ogden hyper-elastic material parameters of the soft tissue within the wrist. As a result, the developed finite element model could predict the contact force based on the displacement of the ultrasound probe into the skin. Several changes were then applied to the original model to optimize its computational efficiency. The final model was shown to be reasonable for real-time applications. As a result of this study, it has been shown that both a load cell and material model can be used to predict the contact force of a human-robot interaction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".