A SYSTEMATIC GAIT-PLANNING FRAMEWORK NEGOTIATING BIOMECHANICALLY MOTIVATED CHARACTERISTICS OF A PLANAR BIPEDAL ROBOT
Bibliographic record
Abstract
Natural human walking possesses three characteristics: (i) gait repeatability, (ii) postural balance, and (iii) highly regulated centroidal angular momentum (CAM). In this paper, a systematic gait-planning framework is presented for the gait planning of a five-link bipedal robot, negotiating those three characteristics. The framework employs a set of task-space variables and a set of gait parameters. Five kinematic and dynamic objective functions are selected, corresponding to the biped's five degrees of freedom (DOFs), incorporating three characteristics. Fusing the equations of five objective functions together, two ordinary differential equations called the framework equations are derived. Assigning desired values to the gait parameters, the framework equations are integrated across the gait cycle, rendering the motion profiles of the task-space variables. A set of simulation results shows that the framework presents gait that successfully negotiates three characteristics. A parametric analysis is then carried out to study the effect of changing the gait parameters on the joint angular displacements and velocities, the postural balance, and the regulation of CAM of the bipedal robot.
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 | 0.000 | 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 | 0.001 |
| 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".