Concurrent mapping and localization for mobile robot using soft computing techniques
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
This paper proposes a novel algorithm combining fuzzy logic (FL) and genetic algorithm (GA) for concurrent mapping and localization (CML) of mobile robot. First, CML is formulated as a multidimensional informed search problem. The search is performed to detect a robot pose which can best accommodate the recent sensor scan in the currently available map. A fuzzy set theoretic approach is used to predict a sample based representation of the state space of possible robot poses and a GA is designed to find out the globally optimal solution from the predicted pose space. The GA evaluates the fitness of poses based on the sensory information and drives the generation gradually towards the globally optimal solution even when the fuzzy prediction is inaccurate. The best fit solution as decided by GA offers the most likely continuation of the currently available map. Experiment on synthetic and real data illustrates the robustness of the algorithm.
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.
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 it