A stochastic approach of mobile robot navigation using customized RFID systems
Bibliographic record
Abstract
Operating a mobile robot using the signal strength of a Radio Frequency (RF) system and/or line-of-sight distances to other known points or RF stations is a challenging task. This problem 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. The current manuscript outlines two different aspects of a mobile robot navigation problem in an indoor environment using Received Signal Strength (RSS) of a customized Radio Frequency IDentification (RFID) system. First, the robot's current location is estimated by a trilateration method where the localization problem is solved through a geometric approach based on Cayley-Menger determinants. The robot position is then better estimated by the application of conventional stochastic filters such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Second, the problem is explored by a set of points on the ground defining a desired path along which a mobile robot is supposed to navigate. The proposed robot navigation system is validated through a number of computer simulation for testbeds of various complexities.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".