Autonomous Locomotion Mode Transition in Quadruped Track-Legged Robots: A Simulation-Based Analysis for Step Negotiation
Bibliographic record
Abstract
Hybrid track/wheel-legged robots combine the advantages of wheel-based and leg-based locomotion, granting adaptability across varied terrains through efficient transitions between rolling and walking modes. However, automating these transitions remains a significant challenge. In this paper, we introduce a method designed for autonomous mode transition in a quadruped hybrid robot with a track/wheel-legged configuration, especially during step negotiation. Our approach hinges on a decision-making mechanism that evaluates the energy efficiency of both locomotion modes using a proposed energy-based criterion. To guarantee a smooth negotiation of steps, we incorporate two climbing gaits designated for the assessment of energy usage in walking locomotion. Simulation results validate the method's effectiveness, showing successful autonomous transitions across steps of diverse heights. Our suggested approach has universal applicability and can be modified to suit other hybrid robots of similar mechanical configuration, provided their locomotion energy performance is studied beforehand.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".