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Record W4403919862 · doi:10.1109/sm63044.2024.10733396

Design and Preliminary Testing of an Aerial Landing Dock for Mobile Robots for Multi-Modal Robotic Systems

2024· article· en· W4403919862 on OpenAlexaff
Troy Radam, Scott Nokleby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsDOCKModalMobile robotComputer scienceRobotEngineeringMarine engineeringAeronauticsReal-time computingArtificial intelligence

Abstract

fetched live from OpenAlex

Despite existing literature exploring multi-agent systems such as Unmanned Aerial Vehicle (UAV) - Unmanned Ground Vehicle (UGV) coordinated systems, few have proposed a landing platform design in order to actively carry and transport a UAV between autonomous missions. The implementation of a UGV as an auxiliary facility to the UAV can provide several benefits such as providing UAVs power, communication, and transport after exhausting its mission capability. The work presented in this paper attempts to address this issue by proposing the design of a landing dock that minimizes the amount of active parts needed to secure and position a UAV. Likewise, to aid in the autonomous landing of the UAV on the proposed landing platform, a Finite State Machine is utilized in order to actively monitor and assign tasks to the UAV during the completion of autonomous missions. Preliminary test results are presented.

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 score0.144
Threshold uncertainty score0.570

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 head0.099
GPT teacher head0.325
Teacher spread0.226 · 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
Published2024
Admission routes1
Has abstractyes

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