Static Balancing of Spatial Parallel Platform Mechanisms—Revisited
Bibliographic record
Abstract
This article discusses the development of statically balanced spatial parallel platform mechanisms. A mechanism is statically balanced if its potential energy is constant for all possible configurations. This property is very important for robotic manipulators with large payloads, since it means that the mechanism is statically stable for any configuration, i.e., zero actuator torques are required whenever the manipulator is at rest. Furthermore, only inertial forces and moments have to be sustained while the manipulator is moving. The application that motivates this research is the use of parallel platform manipulators as motion bases in commercial flight simulators, where the weight of the cockpit results in a large static load. We first present a class of spatial parallel platform mechanisms that is suitable for static balancing. The class of mechanisms considered is a generalization of the manipulator described by Streit (1991, “Spatial Manipulator and Six Degree of Freedom Platform Spring Equilibrator Theory,” in Second National Conference on Applied Mechanisms and Robotics, VIII.B, pp. 1-1–1-6). Then sufficient conditions on the kinematic parameters that guarantee static balancing are derived for this class. Finally a particular mechanism is studied in more detail to show the practicability of its design. [S1050-0472(00)01401-X]
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".