Gremlins: an Architectural Framework for Reconfigurable Autonomous Robots
Bibliographic record
Abstract
The work area of a team of small robots is limited by their inability to traverse a very common obstacle: stairs. We present a complete integrated control architecture and communication strategy for a system of reconfigurable robots that can climb stairs. A modular robot design is presented which allows the robots to dynamically reconfigure to traverse certain obstacles. This thesis investigates the implementation of a system of autonomous robots which can cooperatively reconfigure themselves to collectively travers obstacle such as stairs. We present a complete behaviorand communication system which facilitates this autonomous reconfiguration. The layered behavior-based control system is fault-tolerant and extends the capabilities of a control architecture known as ALLIANCE. Behavior classes are introduced as mechanism for managing ordering dependencies and monitoring a robot's progress through a particular task. The communication system compliments the behavioral control and iimplementsinherent robot failure detection without the need for a base station or external monitor. The behavior and communication systems are validated by implementing them ona mobile robot platform synthesized specifically for this research. Experimental trials showed that the implementation of the behavior control systems was successful. The control system provided robust, fault-tolerant performance even when robots failed to perform docking tasks while recongifuring. Once the robots reconfigure to form a chain, a different control scheme based on gait control tables coordinates the individual movements of the robots. Several successful stair climbing trials were accomplished. Improvements to the mechanical design are proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".