BuPiGo: An Open and Extensible Platform for Visually-Guided Swarm Robots
Bibliographic record
Abstract
The purpose of this paper is to articulate the need for an open, extensible robot platform to support swarm robotic research using vision and to propose one such platform. The platform proposed here is intended for research which trades smaller population size with more sophisticated individual robot capabilities. The validation of proposed swarm robotic algorithms using real-world hardware is essential, but is fraught with difficulty due to the expense and complexity of developing and maintaining multiple operational units. A number of open hardware platforms have been proposed, although most prioritize small size and low cost over advanced capabilities such as vision. We are interested in a number of different research directions which utilize vision as a core capability and find the existing open hardware platforms to be insufficient (and existing commercial platforms too expensive). In this paper we describe a set of desirable characteristics for an open, extensible visually-guided robot platform. We then present our solution, the BuPiGo (pronounced buppy-go), describing the hardware itself and a model developed for simulation purposes. We also present some initial results on using the BuPiGo for visual homing---an individual navigation task that we hope to exploit for swarm tasks in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".