Open-Source Rehabilitation of CRS A255 Robotic Arms: Embedded Fuzzy Control and ROS Integration with Simulation Support
Bibliographic record
Abstract
This work presents the comprehensive retrofitting of two CRS A255 industrial robotic manipulators through mechanical restoration, control system modernization, and integration with simulation environments. Initially rendered inoperative due to hardware degradation and outdated proprietary electronics, the robots underwent structural repairs, cleaning, and the replacement or repositioning of mechanical components, including DC motors and optical incremental encoders. A modular embedded control architecture was developed using microcontrollers, enabling independent actuation of brakes, encoder signal acquisition, and PWM-based motor control. The system was interfaced with the Robot Operating System (ROS) to facilitate real-time communication and control via the rosserial protocol. A fuzzy logic controller was implemented to manage joint positioning without relying on precise dynamic modeling, enhancing robustness and adaptability. Additionally, a digital twin of the manipulator was selected among V-REP (CoppeliaSim) models, allowing bi-directional synchronization between the virtual model and physical hardware. Experimental results validated improvements in angular control accuracy, system responsiveness, and processing latency, confirming the platform’s effectiveness for research and educational applications in robotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".