From Design to Deployment: Decentralized Coordination of Heterogeneous Robotic Teams
Bibliographic record
Abstract
Many applications benefit from the use of multiple robots, but their scalability and applicability are fundamentally limited when relying on a central control station. Getting beyond the centralized approach can increase the complexity of the embedded software, the sensitivity to the network topology, and render the deployment on physical devices tedious and error-prone. This work introduces a software-based solution to cope with these challenges on commercial hardware. We bring together our previous work on Buzz, the swarm-oriented programming language, and the many contributions of the Robotic Operating System (ROS) community into a reliable workflow, from rapid prototyping of decentralized behaviors up to robust field deployment. The Buzz programming language is a hardware independent, domain-specific (swarm-oriented), and composable language. From simulation to the field, a Buzz script can stay unmodified and almost seamlessly applicable to all units of a heterogeneous robotic team. We present the software structure of our solution, and the swarm-oriented paradigms it encompasses. While the design of a new behavior can be achieved on a lightweight simulator, we show how our security mechanisms enhance field deployment robustness. In addition, developers can update their scripts in the field using a safe software release mechanism. Integrating Buzz in ROS, adding safety mechanisms and granting field updates are core contributions essential to swarm robotics deployment: from simulation to the field. We show the applicability of our work with the implementation of two practical decentralized scenarios: a robust generic task allocation strategy and an optimized area coverage algorithm. Both behaviors are explained and tested with simulations, then experimented with heterogeneous ground-and-air robotic teams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".