Landmine detection using an autonomous terrain‐scanning robot
Bibliographic record
Abstract
Purpose Describes a dual‐arm mobile manipulator that can autonomously scan natural terrain using a typical handheld landmine detector in a manner similar to a human operator. Design/methodology/approach Presents a terrain‐scanning robot that consists of two articulated arms mounted on an off‐road remotely operated vehicle. One arm carries a laser and four ultrasonic rangefinders to build a terrain map. The map is used in real time to generate an obstacle‐free path for the second arm that manipulates the landmine detector autonomously. The arms are mounted on the vehicle that is controlled by an operator from a safe distance. Motion planning and control of the robot is carried out using an embedded computer that is linked to a host computer to transmit the detector data and operator commands. Findings Finds that the terrain‐scanning robot can effectively manipulate a relatively large landmine detector on rugged terrain with undulations and obstacles. Research limitations/implications Proposes real‐time motion planning that may be equally applicable to other mobile manipulators. Practical implications Provides a technology that together with state‐of‐the‐art landmine sensors will offer a safe solution for detecting hidden landmines and clearing them from the postwar countries. Originality/value Introduces the concept of a dual‐arm mobile terrain scanning robot for landmine detection in off‐road missions and civilian areas where truck‐mounted detectors are inefficient.
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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 | 0.000 | 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".