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Record W2620839737 · doi:10.18260/1-2--7481

The Trinity College Fire Fighting Home Robot Contest: A Medium For Interdisciplinary Engineering Design

2020· article· en· W2620839737 on OpenAlexaboutno aff
Jacob E. Mendelssohn, David J. Ahlgren

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTSession (web analytics)RoboticsArtificial intelligenceEngineering educationVirginia techRobotComputer scienceEngineeringEngineering managementLibrary sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 3520 The Trinity College Fire-Fighting Home Robot Contest: A Medium for Interdisciplinary Engineering Design David J. Ahlgren, Jacob E. Mendelssohn Department of Engineering Trinity College, Hartford CT 06106 Introduction In this paper we introduce the Trinity College Fire-fighting Home Robot Contest, describe some of the engineering design problems it presents, and discuss examples of work done at Trinity. Held annually on the Trinity College campus in Hartford, CT since 1995, this is the largest robotics competition in the U.S. open to contestants of any age, affiliation, ability, or experience. The goal of the contest is to stimulate interest in robotics and to encourage invention by persons of all ages. A $1,000 first prize is awarded in both the junior division (high school and younger) and the senior division (all others). The contest was expanded in 1998 to include affiliated regional events that use the Trinity College rules; at this writing, events are scheduled in Fort Worth, Calgary, and Seattle. Winners from the regional contests will compete in the final event, held at Trinity on April 19, 1998. Participation engages engineering students and professionals in a motivating, open-ended interdisciplinary project. Design of a fire-fighting mobile robot is a challenge that is appropriate, for example, as a senior engineering design project. The object is to develop a computer-controlled, autonomous machine that can navigate through a 8 ft. by 8 ft. maze, find a fire (a lit candle), and extinguish it in minimum time. The robot must operate without human intervention; radio control and joystick control are not permitted. The walls of the maze (painted white) are 13" high, the hallways are 18" wide, and the floor is flat black. Thus the robot simulates the real-world operation of a robot performing a fire-security operation in a single-story home. The maze geometry, which is known beforehand by the contestants, includes four rooms and connecting hallways. The robot does not know where the fire is located, and the fire can be in any of the rooms. Before extinguishing the flame, the robot must navigate to within 12" of it and show that it has recognized the flame. Each robot makes three runs, which begin at a designated starting spot. The score is the sum of the fastest two run times, multiplied by reduction factors for: 1) reliability (success on all three runs); 2) obstacle avoidance ability; 3) ability to return to the starting spot after extinguishing the candle; and 4) ability to trigger the robot's run using a 3.5 KHz tone that simulates a smoke alarm. The 1998 contest encourages the development of robots that do not rely on dead reckoning. Robots will receive a deduction for succeeding when ramps, which add uncertainty about path lengths, are placed in the maze.1 Engineering Design Problems Development of a fire-fighting mobile robot is a constrained optimization problem that can be 1 Full contest rules are found at the Web address: http://www.trincoll.edu/~robot.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.104
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0060.003
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1040.017

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 head0.044
GPT teacher head0.273
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations7
Published2020
Admission routes1
Has abstractyes

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