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Record W2802888198 · doi:10.1139/tcsme-2015-0038

DEVELOPMENT OF TRACKING SYSTEM FOR MOBILE ROBOT USING IMAGE RECOGNITION ALGORITHMS

2015· article· en· W2802888198 on OpenAlexvenueno aff
Kuo-Lan Su, Boyi Li, Kuo-Hsien Hsia

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2015
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
FundersNational Science Council
KeywordsMobile robotComputer scienceImage processingMicroprocessorComputer visionRobotController (irrigation)Artificial intelligencePID controllerTracking (education)EngineeringEmbedded systemControl engineeringImage (mathematics)

Abstract

fetched live from OpenAlex

In this paper, an intelligent mobile robot using image processing technology is developed. The mobile robot contains an image system, a loading platform, a balance control system, a PC-based controller, four ultrasonic sensors and a power system. We develop a PC-based control system for image processing and path planning. The mobile robot can track a moving target and adjust the loading platform by the balance control system simultaneously. The image processing based on OpenCV uses two different tracking methods to track moving targets: MTLT (Match Template Learning Tracking) and TLD (Tracking, Learning and Detection). The efficiency of both methods for tracking the moving target on the mobile robot is compared here. The balance control system, with a HOLTEK Semiconductor Company’s HT66F Series 8-bit microprocessor as the processor, uses the PID control law according to the feedback signals of the inclinometer sensor to control the balance level of the loading platform.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.156
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.058
GPT teacher head0.256
Teacher spread0.197 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2015
Admission routes1
Has abstractyes

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