Fighting COVID: An Autonomous Indoor Cleaning Robot (AICR) Supported by Artificial Intelligence and Vision for Dynamic Air Disinfection
Bibliographic record
Abstract
Due to the sever circumstances in the global pandemic, there has been an immense need for disinfectant robot technology. This pandemic has made people much more aware about the severity of virus transmission in public areas. This prompts society to be much more aware of the need to maintain a clean environment. The purpose of this paper is to present the design principles of an Autonomous Indoor Cleaning Robot (AICR) developed to reduce the spread of COVID-19 in indoor environments such as small shops and office settings. Its main purpose is to proactively disinfect the air and maintain a clean breathing environment by actively targeting populated areas with the use of a vision system, using Visual Simultaneous Localization and Mapping (VSLAM) technology. Currently there are other air disinfection products on the market also making use of a combination of a High-Efficiency Particulate Absorbing (HEPA) air purifier and Ultra Violet (UV) light to kill airborne viruses like the Coronavirus. However, all of these are stationary with lack of intelligence machines that have to be kept or manually wheeled from room to room. The device proposed in the paper is a fully autonomous air purifying device capable of going to certain critical regions of the indoor environment to disinfect the air in that area without any human interaction. The stationary purifiers should be much more powerful covering a larger area which makes them very expensive. In contrast, the developed autonomous air purifier needs much less power consumption compared to static purifiers, with the advantage of intelligently and dynamically learning the status of the room using the information captured from the occupancy, itself, and the environment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".