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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".