Kinematic and Potential Energy Analysis of Self-Adaptive Robotic Legs
Bibliographic record
Abstract
This paper presents how the kinematic and potential energy analysis of self-adaptive robotic legs can help to improve their performances with respect to their ability to overcome obstacles and the required actuation torque to do so. Self-adaptive leg mechanisms, inspired by the underactuated linkages used in grasping, generally rely on a single degree of freedom (DOF) to generate a trajectory at its endpoint that is appropriate for walking applications. When colliding with an unexpected obstacle, a second DOF in the leg automatically engages and creates a motion allowing the leg to overcome said obstacle. Since this behavior is obtained mechanically, with no sensor or control, these robotic legs are referred to as self-adaptive. In this paper, the conditions for the passive adaptation to obstacles are first briefly recalled. Then, the range of obstacles for which this adaptation is possible is determined through the analysis, using potential energy, of the mechanism workspace and it is shown how the results are connected to its kinematics. In particular, the influence of the shape of the terminal link of the leg is discussed with two compared examples. Finally, practical designs and especially the relative advantages of various locking mechanisms, required to improve stability during the support phase of the leg trajectory, are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".