Bibliographic record
Abstract
The objective of this thesis is to study the walking control of biped robots including imbalance walking phase. Thus we dedicated to the stability analysis of periodical walking with under-actuation. Firstly, a planar biped robot with point feet is studied. Some tools for stability analysis such as Poincaré return map, Zero Dynamics and Hybrid Zero Dynamics are discussed. Two control laws based on joint reference motion (common in robotics) are proposed and compared with a control law for tracking parameterized reference trajectory (free temporal evolution). Next a biped robot with feet moving in 3D is discussed. In view of the importance of ZMP (Zero Moment Point) for the walking stability, it is regulated to its desired values. Moreover, a feedback control of the orientation of the swing foot is used to ensure a proper landing. As a result, some controlled outputs have been defined, so the robot can be dealt with like an under-actuated system. The approaches developed previously are then taken to obtain a stable walking. In addition, a steering control law is proposed to control the walking direction of the robot to pass through a door and reach a destination with a mild curvature. Finally, the proposed controller is extended to a biped robot with arms and a foot rotation phase that describing the stance heel lifts from the ground and the stance foot rotates about the toe is included in the single support phase. The proposed control can be naturally extended to this imbalance phase due to the control of ZMP, and the simulation results validate it is effective to obtain stable walking.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".