Autonomous Gait Switching Strategy for Cross Domain Robots based on Bayesian Networks
Bibliographic record
Abstract
In complex amphibious environments, cross-domain robots require enhanced adaptability to diverse terrains to improve operational efficiency and ensure safety. This paper proposes a Bayesian network (BN)-based autonomous gait switching decision-making method for cross-domain robots. First, the Max-Min Hill-Climbing-K2 (MMHC-K2) algorithm is employed to learn the BN structure. Subsequently, expert knowledge and sample data are integrated to determine prior probabilities for input nodes. Finally, the posterior probability of the decision is solved using Bayesian formula to complete the parameter learning of the BN model, thus completing the model construction. Environmental information acquired through onboard sensors is input into the established BN model, which calculates the optimal decision scheme with maximum probability. The decision output includes both recommended gait patterns and detailed parameter configurations. The derived gait control parameters are fed into the Central Pattern Generator (CPG) control network, where six Hopf oscillators resolve leg-specific phases. A mapping function then outputs rotation angles for the robot’s arc-shaped legs, enabling motor-driven gait switching. This mechanism allows the robot to traverse complex terrains with appropriate locomotion patterns. To verify the effectiveness of the method, terrain crossing simulations were conducted in a water land transition environment. Simulation results demonstrate that the robot can effectively make gait switching decisions based on sensor inputs, successfully implement these decisions through CPG network control, and ultimately achieve stable traversal on challenging terrains.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".