Proximity and Contact Sensing with Instrumented Compliant Wrist for Close Guidance of Robotic Manipulators
Bibliographic record
Abstract
Compliance in robotic systems has been exploited to allow rigid mechanisms to come into contact with complex and possibly fragile objects. By incorporating compliance and instrumentation into a single device nearby objects can be detected before direct contact occurs. That way, safer and smoother robot guidance can be achieved both while approaching and while touching surfaces. Furthermore, the path planning and control problem is simplified as position based algorithms can be used regardless of the state of the system, be it in free motion or constrained motion, or even during transitions between the two modes. This paper presents the design and experimental validation of a lightweight, low-cost and stand-alone instrumented compliant wrist mechanism which can be mounted on the tool plate of any rigid robotic manipulator. Embedded arrays of infrared sensors provide distance measurements. Each is finely tuned via a novel calibration procedure that overcomes inter-sensor variability. All signal processing is also embedded and wireless transmission connects the device to the robot controller to support path control. Real-time acquired measurements on the position and orientation of surfaces located in close proximity or in contact with the robot’s end effector permit close guidance of its operation. Experimental work demonstrates how the device provides physical compliance to prevent large impact forces to occur during non-contact to contact transitions by the manipulator’s end effector. It also demonstrates the stability and accuracy of the device outputs. Primary applications of the proposed instrumented compliant wrist include smooth surface following in manufacturing and safe 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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".