Design and development of an autonomous mobile patrol robot
Bibliographic record
Abstract
Autonomous mobile robots (AMRs) are especially useful for completing tasks that humans find repetitive or unsafe.An industry that would benefit from the integration of an AMR is security, where a robot could be tasked with patrolling areas to report potential concerns, hazards or violations based on the environment.To research and experiment with these possibilities, a versatile robotic platform capable of operating outdoor and indoor amongst cars and humans is required.Commercial and open-sourced AMRs are available and range in sizes; however, there is no existing mobile robotic system in the market that can be directly adopted in the application with all required capabilities.Therefore, a newly designed mobile robotic system, specifically for patrol and policing applications, is proposed in this paper.It is a robust 1/3 rd scale car-like robot with a modular, and reproducible hardware architecture.The design and development of the SPR is presented as it aims to expand the possibilities of research and validation in simulation and experimentation.The test result of the designed robot showed its ability to navigate and localize in an environment, displaying its readiness for autonomy.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".