Explosive Projectile Detection with an Arduino-Controlled Robot
Bibliographic record
Abstract
Proposed Explosive Ordnance Disposal Using Arduino is a mixture of two applications which are espionage and ordnance detection. The Mini Spy Robot is a prototype robot with an attached camera. The motors will be controlled by transmitters, which in turn will then be controlled via Remote using a Wi-Fi module or a Zigbee module for improved access. The goal is to build a military-field robot that is equipped to recognize explosives that stand in the way of landmines and that is remotely controlled through a module. It can be used to screen War field. The robot can move in any directions controlled by the remote. This robotic framework is also used for bomb identification. The control gadget of the whole framework is Arduino. This reduces circuit complexity and increases execution speed. At any point landmines or bombs are identified, the alarm is triggered via the Wi-Fi module. The Arduino used in the task is customized using Embedded C language. After detecting the bomb, the functionality is added to manually control the robot and defuse the bomb using the robot arm, while fixing the knife on the end of the arm so that it is easy to defuse the bomb without human touch.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.010 | 0.003 |
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".