Localization of a Team of Heterogeneous Robots for a Distributed Sensing Task
Bibliographic record
Abstract
This paper presents a methodology for localizing the members of a heterogeneous team of mobile robots in an unstructured and unknown environment. In this approach robots are being divided into two groups: (1) localizers, and (2) missioners. The localizers are equipped with precise non-contact sensors such as laser range finders and/or vision. Therefore, they can find the distance, bearing and orientation of the other robots with high accuracy, as long as they are in localizer's field of view. The missioners would be equipped with simple odometry and/or local range sensors (i.e., SONAR, Infrared etc.) for collision avoidance. All robots are capable of cross communication by passing messages via a network. When a missioner is being detected by a localizer, it receives a message from the localizer which contains missioner's precise position information. Hence it is able to update its belief about its position on fly. The effect of this methodology in a distributed sensing task in the context of solving a multi-depot traveling salesman problem (MDTSP) has been briefly addressed where missioners go through sub-optimal paths to their target positions being assigned through an auction process
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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".