Localization of multiple robots with simple sensors
Bibliographic record
Abstract
This paper presents a distributed particle filter algorithm for localizing multiple mobile robots that are equipped only with low cost/low power sensors. This method is applicable to multi-micro robot systems, where size limitations restrict sensor selection (e.g. small infrared range finders). Localization of three robots in a known environment is conducted by combining measurements from a small number of simple range sensors with inter-robot distances obtained through an acoustic range finder system. The localization problem is formulated as estimating the global position and orientation of a single triangle, where corners of the triangle represent the positions of robots. The robot positions relative to the centroid of the triangle are then determined by trilateration using the inter-robot distance measurements. Each robot uses an identical particle filter algorithm to estimate the global position of the triangle. The best estimates determined by each particle filter are distributed among the robots for use in the following iteration. Simulations demonstrate the ability to perform global localization of three robots, each using a compass and two range finders. The results illustrate that this method can globally localize the robot team in a simulated indoor environment The results are compared to simulations where robots have access to only their own sensor data, which are unable to successfully localize under equivalent conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".