Bibliographic record
Abstract
Remote sensing of buried explosives has continuously been a great concern, especially for detecting landmines jeopardizing the human lives and economic development of the war-torn countries. This dissertation describes the software development for a mobile terrain scanning robot capable of autonomously manipulating a typical handheld detector for remote sensing of buried landmines in a manner similar to a human operator. The autonomous manipulation of the detector on unknown terrain requires acquiring sensor data for developing an online terrain map and generating an obstacle free path for the end effector of the robot. Thus, the software includes a twofold process of map building and path planning that are specifically designed for a real-time platform to be orchestrated with the other functions of the robot. Map building features a distributed sensor fusion system to tackle the uncertainties associated with the sensor data. It provides local terrain maps by fusing the redundant measurements and the complementary data obtained from competitive rangefinders and joint position sensors, respectively. The fusion takes place in a compound data processing module that includes a batch processing filter, a static filter, and a fuzzy adaptive Kalman filter. The Kalman filter requires a dynamic model of the process so that a novel stochastic model is introduced for the terrain undulations. An important parameter of the model that significantly influences the output of the filter is the standard deviation of the probability distribution of the process disturbances modeled by white noise. A systematic fuzzy modeling technique is used to determine the standard deviation based on the terrain type and adapt the filter, accordingly. The outlier rejection is carried out using the Mahalanobis distance between the estimates and the new measurements. Path planning determines the desired joint coordinates of the robot to move the detector at a constant distance from the ground when the normal to the detector plate is maintained parallel to the local normal of terrain. Unlike the traditional methods, the path is generated in the non-Cartesian coordinate frame of the sensors to avoid a great deal of transformations involved in reproducing the terrain map in the Cartesian coordinate frame. The software has been successfully implemented into the Mine Detection Robot (MR-2) manufactured by Engineering Services Inc. (ESI) to synthesize the autonomous manipulation of a metal detector.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".