ANALYSIS OF JOINT FAILURES ON THE LATERAL UNDULATION GAIT OF A ROBOTIC SNAKE
Bibliographic record
Abstract
This paper describes the development of a biologically-inspired hyper-redundant wheeled snake robot and a corresponding computer simulator to study the effects that joint failures have on the resulting lateral undulation motion. Experiments and corresponding simulations were carried out to study the robotic snake’s lateral undulation gait as power to individual joints was turned off. The results showed that joint failures were most detrimental to the snake’s lateral undulation gait when they occurred in the front half of the snake, while joint failures occurring between the midpoint and tail of the robotic snake were found to be less critical and generally resulted in slight lateral drifts as the forward motion progressed. To help compensate for joint failures in the tail-half part of the robot, a bias term was added to the control algorithm. For the conditions tested in this research, the use of a bias term appeared to be effective at reducing the lateral drift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".