Indoor robot navigation through intelligent processing of RFID signal measurements
Bibliographic record
Abstract
The purpose of this manuscript is to present a novel non-vision-based indoor mobile robot navigation technique with an intelligent processing of received signal strength (RSS) measurements using a customized Radio Frequency IDentification system. The navigation problem of a mobile robot has been traditionally solved by several approaches suggested in the literature. Among the most common shortcomings of those approaches are the use of excessive number of sensors or multiple reference RF stations for the robot to estimate its location in an indoor environment. Moreover, a spatial layout or cost problems limit the applicability of those approaches in many real-world robotic systems. The current work is devoted to developing a mobile robot navigation system where RSS measurements are provided to the robot by a customized RFID reader mounted on it. For it to navigate, the robot simply applies necessary actions to its actuators based on the intelligent processing of those RSS measurements. The customized RFID reader architecture is simulated using the comprehensive electromagnetic commercial software, FEKO. The proposed navigation system is evaluated through a number of computer simulations. It is shown through these simulations that a mobile robot has the ability to successfully navigate along a pre-defined path in an indoor environment regardless of the path's complexity.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".