An RFID-Based Robot Navigation System with a Customized RFID Tag Architecture
Bibliographic record
Abstract
A major component of a mobile robot system is the ability to navigate accurately in unknown environments with little or no human intervention. In this paper, we present a modular and cost-effective navigation technique incorporating signals from RFID tags, an RFID reader, and a fuzzy logic controller (FLC). The RFID tags are placed at 3-dimensional positions in the robot's workspace in such a way that the lines linking their projection points on the ground define "free-ways" along which the robot is desired to navigate. The RFID reader is mounted on the mobile robot to communicate with the RFID tags to determine the robot's position. The FLC is then applied to guide the robot along a pre-defined trajectory in an unknown working environment. For this purpose, we introduce two minor changes to the RFID tag architecture while keeping that of the RFID reader unchanged. A simplistic circuit and a primitive microcontroller are added to the RFID tag to compute the signal's power received by the tag and encode it within the tag ID, respectively. This way, virtually any commercially available RFID reader can be used without the need for any special customization. The performance of the proposed navigation scheme is evaluated through several numerical simulations.
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 | 0.001 | 0.000 |
| 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".