Review of Kinematics for Minimally Invasive Surgery and Tele-Echography Robots
Bibliographic record
Abstract
This paper deals with the survey of kinematic structures adapted to specific medical robots: minimally invasive surgery (MIS) and tele-echography. The large diversity of kinematic architectures that can be found in medical robotics leads us to perform a statistical analysis to inform and guide design of medical robots. Safety constraints and some considerations in design evolution of medical robots are presented in this paper. First, we describe the spectrum of medical robots in minimally invasive surgery and tele-echography applications and particularly the variety of kinematic architectures used. We present the robots and their kinematic architectures and highlight differences that occur in each medical application. We perform a statistical analysis which can serve as a resource in topological synthesis for each specific medical application. Safety is an important specification in medical robotics, and for that reason we show the means used to take into account this constraint. This study demonstrates that the nature of medical robots implies specific requirements leading to different kinematic structures. The statistical analysis gives information on choice of kinematic structures for medical applications (minimally invasive surgery and echography). The safety constraint as well as the interaction between doctor and robot leads to investigate new mechanical solutions to enhance medical robot safety and compliance. We expect that this paper will serve as a significant resource and help the design of future medical robots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".