Evaluation of Dynamic Performance of Non-Spherical Parallel Orientation Manipulators through Bond Graph Multi-Body Simulation
Bibliographic record
Abstract
Dynamic performance of a parallel orientation manipulator requiring a small form factor is primarily determined by the dynamic response of the corresponding actuators. Hence, identifying a suitable kinematic architecture for such a manipulator is constrained by the choice of the actuator, which is based on several application-specific requirements such as dynamics, compactness, positioning accuracy, etc. The kinematic topology of each of the two architectures that can accommodate a prospective voice coil linear motor consists of three identical closed loops. These two candidate architectures are compared, one of which requires the actuator bodies to rotate, thereby introducing inertial effects that impact performance. This paper quantifies the performance improvements when no such inertial effects are present. Through dynamic simulation of the multi-body systems resulting from the candidate architectures, the architecture that provides superior performance in terms of settling times and expended energy in a random robotic maneuver can be identified. For this purpose, the multibody models of the candidate architectures were developed and subsequently a Monte-Carlo performance benchmarking study was conducted. This study identified the architecture in which the actuator bodies did not rotate to be more desirable, as indicated by lower settling times and lower expended energy in executing a set of random maneuvers. The multi-body dynamic models were developed in bond graph formalism because of the flexibility it offers for modeling closed loop kinematic systems that are free of causal conflicts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".