Low-Impedance Displacement Sensors for Intuitive Physical Human–Robot Interaction: Motion Guidance, Design, and Prototyping
Bibliographic record
Abstract
This article provides a general framework for the use of low-impedance displacement sensors mounted on the links of a serial robot to provide an intuitive physical human–robot interaction. A general formulation is developed to handle the motion guidance problem, i.e., the mapping of the measured motion of the sensors into the required robot joint motions to provide intuitive responsiveness. The formulation is general and can be applied to any architecture of serial robot with any number of displacement sensors each having an arbitrary number of degrees of freedom. Then, the design of a novel three-degree-of-freedom low-impedance displacement sensor is presented as a particularly effective instantiation of the general concept. Partial force balancing is used to reduce the required elastic return action, thereby ensuring the low impedance of the interaction. A prototype of a three-degree-of-freedom displacement sensor is then introduced. Two such sensors are mounted on the links of a custom-built five-degree-of-freedom robot in order to demonstrate the proposed approach. Experimental results are provided and comparisons with other collaborative robots are given. It is shown that the proposed sensors and motion guidance approach yield very intuitive low-impedance interaction involving very low interaction forces.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".