MétaCan
Menu
Back to cohort
Record W7132887395

Uncertainty management method for a terrain scanning robot

2002· dissertation· W7132887395 on OpenAlex
Homayoun Najjaran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTSpace · 2002
Typedissertation
Language
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
FundersUniversity of Toronto
KeywordsTerrainSensor fusionKalman filterMotion planningProcess (computing)Mobile robotObstacle avoidanceFilter (signal processing)ObstacleRobot
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.657
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.382
Teacher spread0.351 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it