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Obstacle Avoidance of Multiple Manipulators Based on 3D Artificial Potential Field Method

2021· article· en· W3212961470 on OpenAlexaff
Fei Xiao, Bing Li, Hailin Huang, Fengfeng Xi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsToronto Metropolitan University
FundersShenzhen Research and Development Program
KeywordsCollision avoidanceObstacle avoidancePotential fieldObstacleTrajectoryComputer scienceField (mathematics)Control theory (sociology)Manipulator (device)CollisionMotion planningRobot manipulatorArtificial intelligenceRobotControl engineeringEngineeringMathematicsMobile robotPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper proposes a multiple manipulators obstacle avoidance algorithm based on the 3D artificial potential field method. The distance model of the manipulators is established to detect the possible collision of the manipulators. The 3D artificial potential field method is used to deal with the obstacle avoidance problem in the operation of multiple manipulators. Consequently, a smooth collision-free trajectory can be obtained, which is dynamically adjusted according to the parameters of potential field. The experimental results with the UR manipulator illustrate that the developed system is efficient and fast in detecting collisions as well as planning paths.

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)0.000
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 score0.350
Threshold uncertainty score0.494

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.026
GPT teacher head0.280
Teacher spread0.253 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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