Fault detection in mobile robots using sensor fusion
Bibliographic record
Abstract
Fault detection and isolation in mobile robots has become a challenging task primarily due to uncertain and dynamic operating environments. The design of model-based fault detection methods would not be a practical real-time solution in view of the dynamic and uncertain nature of the problem. Also, conventional single-sensor approaches have limitations in practical applications. In this paper, a method of fault detection and isolation (FDI) based on a multi-level data fusion and response (behavioral) analysis technique is presented. The proposed FDI scheme mainly consists of pre-processing, sensor-fusion, a conflict monitoring unit, a confidence level computation unit, a high-level information fusion unit and a fault isolation unit. The developed FDI method is implemented in a simulated robot environment employing IR/camera fusion for navigation and obstacle avoidance. The fusion-based FDI method is tested under faults in camera and IR sensor. With the developed approach, faults are detected in a timely manner and isolated accurately. Also, with the incorporation of sensor fusion, reliable and accurate sensor information is adaptively fused and fault tolerance is achieved under camera/IR sensor faults.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".