Soft Sensitive Skin for Safety Control of a Nursing Robot Using Proximity and Tactile Sensors
Bibliographic record
Abstract
This article presents a novel robot skin that integrates both proximity and tactile sensors in a nursing robot to maximize the safety of patient transfer tasks. Two types of sensors are mounted on a honeycomb substrate made of flexible photosensitive resin. The proximity sensor consists of several distance sensor arrays, each with 16 laser sensors connected to a microprocessor via an inter-integrated circuit bus. The sensor array is made of a flexible printed circuit, and the distance between the robotic arm and object is measured based on the time-of-flight principle. The tactile sensor consists of multiple 125 mm$\times125$mm pressure sensor patches, each integrating 64 piezoresistive pressure sensors. This article presents the design and manufacture of the sensitive skin and proposes the safety control strategies of a nursing robotic arm using sensor information. In particular, by employing proximity sensors to detect approaching objects, the robotic arm can avoid high-speed collisions. The posture of the arm can be adjusted by using the tactile sensors to prevent the patient from slipping off and failure of the robotic arm. Preliminary experiments were conducted using the proposed sensitive skin and our nursing robot. The results are presented to demonstrate the accuracy of the sensor data and feasibility of the safety control strategies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".