Reconfigurable distributed real-time processing for multi-robot control: Design, implementation and experimentation
Bibliographic record
Abstract
Sophisticated robotic applications require systems to be reconfigurable at the system level. Aiming at this requirement, this paper presents the design and implementation of a software architecture for a reconfigurable real-time multi-processing system for multi-robot control. The system is partitioned into loosely coupled function units and the data modules manipulated by the function units. Modularized and unified structures of the sub-controllers and controller processes are designed and constructed. All the controller processes run autonomously and intra-sub-controller information exchange is realized by shared data modules that serve as a data repository in the sub-controller. The dynamic data-management processes are responsible for data exchange among sub-controllers and across the computer network. Among sub-controllers there is no explicit temporal synchronization and the data dependencies are maintained by using datum-based synchronization. The hardware driver is constructed as a two-layered system to facilitate adaptation to various robotic hardware systems. A series of effective schemes for software fault detection, fault anticipation and fault termination are accomplished to improve run-time safety. The system is implemented cost-effectively on a QNX real-time operating system (RTOS) based system with a complete PC architecture, and experimentally validated successfully on an experimental dual-arm test-bed. The results indicate that the architectural design and implementation are well suited for advanced application tasks.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".