Autonomous Mobile Robot Design and Testing for Data Center Monitoring Mission
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a cost-effective autonomous mobile robot (AMR) designed for remote data center monitoring, motivated by the need to enhance operational efficiency and reduce human intervention in critical infrastructure. The proposed model integrates a TurtleBot Kobuki base with a novel SRF10-based virtual bumper system, RPLIDAR scanner, and GoPro camera, enabling autonomous navigation, obstacle avoidance, and high-resolution photo capture in data center corridors. Developed through a university-industrial collaboration, the prototype leverages ROS-compatible subroutines to minimize development costs. Laboratory and real-world tests validate the AMR’s performance, achieving 92% navigation success and 94.2% gap navigation accuracy, while identifying limitations like positioning errors and low-clearance obstacle detection. Proposed upgrades, including a faster LIDAR and enhanced computing power, aim to meet industrial standards. This study establishes a foundation for scalable, automated monitoring solutions across data centers and similar sectors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it