Mobile robot navigation using particle swarm optimization and noisy RFID communication
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Among the major shortcomings of modern mobile robot navigation systems are their dependence on an excessive number of sensors and sensor types, and their prohibitively high computational complexity which often requires an additional data processing board to handle it. The present manuscript presents a radio frequency identification (RFID)-based navigation approach where a number of tags are attached at predetermined locations in the environment to guide a robot equipped with an RFID reader in tracking its predefined trajectory. Due to the typical excessive noise characterizing RF signals in general, redundant information extracted from the tags is exploited with the help of a particle swarm optimization (PSO) algorithm to enhance the robotpsilas position approximation accuracy. The effectiveness of the proposed scheme is demonstrated through computer simulations of different testbeds with various complexities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it