A Laser Intensity Based Autonomous Docking Approach for Mobile Robot Recharging in Unstructured Environments
Bibliographic record
Abstract
Autonomous recharging is a fundamental requirement for autonomous mobile robots in order to allow them to work continuously without human intervention, and autonomous docking to a charging station is a challenging yet promising research area. In particular, the docking task becomes even more complicated in unstructured human environments when the charging station’s location is not fixed and when there are dynamic obstacles moving around. This paper presents a laser intensity based autonomous docking approach, which allows a mobile robot to recharge its battery autonomously in unstructured environments. Laser reflection intensity for a self-adhesive reflective tape is quantitatively investigated, and the sufficient/necessary conditions for successful reflector detection are discussed. Using the proposed reflector detection technique, the charging station can be easily distinguished from similar objects in an unstructured environment. Comparing to the traditional docking methods, the developed approach is easier to implement and can significantly improve the reliability of autonomous docking and recharging. Extensive experiments were conducted to verify the effectiveness of the developed autonomous docking approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".