Learning-Based Terrain Identification With Proprioceptive Sensors for Mobile Robots
Bibliographic record
Abstract
High accuracy of terrain identification is essential for intelligent control of tracked mobile robots. In this article, a learning-based identification framework is proposed to achieve precise driving torque prediction. Experiments under straight-line and steady-state turning maneuvers were conducted to develop terrain identification in three similar terrains. A multiple deep belief networks is applied as the identification layer with three kinds of signal sources. An equivalent weight algorithm with training experience effectively integrates the results from different signal sources to improve the identification accuracy. With the experiment and identification results, a method combining numerical approximation and Gaussian process (GP) is presented to predict driving torque. A combined Gaussian kernel with long- and short-term characteristics is selected to enhance prediction performance. The results from combined signal sources under straight-line maneuvers yield over 98% accuracy, which exceeds that from other sources. The integration algorithm obviously improves the identification accuracy and stability compared with a single signal source. The influence of window length in GP is explored with hyperparameters and results. The performance of torque prediction in different terrains is analyzed with inner and outer tracks. Compared with existing methods, the results validate the effectiveness and superior performance of the proposed framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".