Impact of Kinematic Structure on the Force Displayability of Planar Passive Haptic Devices
Bibliographic record
Abstract
Haptic devices containing passive actuators, such as controllable brakes or dampers, are an attractive alternative to their motor-driven counterparts due to intrinsic stability and improved impedance bandwidth. Passive actuators cannot generate energy and, therefore, the output force can only oppose the applied velocity. In the same way the kinematic structure of traditional manipulators is designed to maximize dexterity and manipulability, one must consider adapting the device topology to optimize force displayability when designing passive actuators. This article introduces a set of metrics to evaluate and compare the performance of 2-degree-of-freedom (DOF) serial and parallel passive haptic devices. These metrics consider the impact of the kinematic structure on the force displayability according to the directions of the device velocity and desired force. Applying these metrics to nine manipulators revealed that: 1.) serial manipulators can generate passive forces in all directions equally regardless of the link length ratio; 2.) the base link length of five-bar parallel manipulators strongly influences passive force displayability; 3.) five-bar parallel manipulators with the base link length of zero can generate the widest range of passive forces when all links have the same length. The novel performance metrics presented in this paper can aid in the design of 2-DOF passive haptic devices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".