ON IMPROVING BIPEDAL WALKING ENERGETICS THROUGH ADJUSTING THE STIFFNESS OF ELASTIC ELEMENTS AT THE ANKLE JOINT
Bibliographic record
Abstract
Impact at each leg transition is one of the main causes of energy dissipation in most of the current bipedal walking robots. Minimizing impact can reduce the energy loss. Instead of controlling the joint angle profiles to reduce the impact, which requires a significant amount of energy, installing elastic mechanisms (with adjustable stiffness) on the robots structure is proposed in this paper, enabling the robot to reduce the impact, and to store part of the energy in the elastic form and return it to the robot. The conceptual design of an adjustable stiffness artificial tendon is proposed which is added to the ankle joint of a bipedal walking robot model. Simulation results on the stance phase demonstrate significant improvements in the energetics of the bipedal walking robot by proper stiffness adjustment of the tendon as compared to using a single linear spring. A controller based on energy feedback is designed to automatically adjust the stiffness of the tendon. Computer simulations illustrate improvements in performance of the energetics of the bipedal walking robot in consecutive walking steps while the stiffness of the tendon is adjusted properly.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".