Firefighting robot with video full-closed loop control
Bibliographic record
Abstract
In view of the many problems in firefighting robots, such as complicated flame positioning, poor environmental adaptability, and difficult installation and debugging, a new firefighting robot design is presented with video full closed-loop feedback to extinguish ground fire in this paper.The firefighting robot consists of a 2-DOF robot, a monocular camera, and a controller.The monocular camera installed on the second link of the robot is utilized to detect and locate ground flames.The robot can dynamically adjust the water landing point to track a flame in real-time through motion control, as the camera is specifically designed with an additional infrared narrowband pass filter and a filter-switching mechanism.An algorithm of detecting and positioning for flame and water landing point is proposed based on image processing and robotic kinematics.Experimental results show that the firefighting robot with video full closed-loop feedback can realize real-time flame detection, location, and sprinkler, and can dynamically track fire location within the monitoring scope.The distance error between fire and the landing point of the water jet can be controlled within a narrow range.Moreover, this firefighting robot is easy to install, debug and have good environment adaptability, and provides efficient and safe solutions for complicated firefighting environment.At the same time, due to its small size and convenient calibration features, this firefighting robot is especially suitable for large space environments.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".