Development of Fail-Safety System for Building Wall Cleaning Robot
Bibliographic record
Abstract
Development of Fail-Safety System for Building Wall Cleaning Robot J. Huh, S. M. Moon, S. W. Hwang, S. M. Yoon, D. Hong Pages 206-212 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: Recently, there is growing trend to build the high-rise and install curtain walls. Following this trend, we developed robot for building wall cleaning work, which moves along Built-in guide rail of building. Because it moves attached at building wall for cleaning work, it can be seriously affected by potential threats, like earthquake, strong wind, malfunction, and construction error of built-in guide rail. In order to cope with those threats actively, this paper presents the Fail-Safety system. The building wall cleaning robot consists of two moving system: The Horizontal Moving System which mainly do maintenance work, and The Vertical Climbing System which transport the horizontal moving system floor by floor. We apply the Fail-Safety system to these systems. The Fail-Safety system consists of sensors to detect external situation, and, with information of sensors, give instruction for what to do. This robot system is installed with four kinds of sensors: shock sensor, infrared ray sensor, laser sensor, magnetic sensor. First, shock sensor detects external shock during cleaning work. When shock sensor detects big shock, the robot returns to starting point to inspect how damaged it is. Second, infrared ray sensor detects damage of built-in guide rail. It is to prevent destruction of robot caused by moving along damaged rail. Third, laser sensor gives notice about where obstacle is. It is for robot to avoid crash with obstacle and decrease damage. Fourth, magnetic sensor detects magnetic points, which are installed in rail at regular intervals, and helps robot to find its position, based on location of magnetic points detected. If robot is damaged by external shock and its encoder, which gives information of location to it, is not working, magnetic sensor will give information of robots location to robot. And then, robot regulates its velocity depending on position of it, and safely returns to starting point. The Fail-Safety system in this paper is for building wall cleaning robot to sense external threats, and prevent getting worse. Applied to this active protection system, making safe environment of maintenance work is possible for robot system. Keywords: Building wall cleaning, Fail-Safety DOI: https://doi.org/10.22260/ISARC2013/0022 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".