Transportation of small objects by robotic throwing and catching: applying genetic programming for trajectory estimation
Created by W.Langdon from
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- @Article{GAYANOV:2018:IFAC-PapersOnLine,
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author = "Ruslan Gayanov and Konstantin Mironov and
Ramil Mukhametshin and Aleksandr Vokhmintsev and
Dmitriy Kurennov",
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title = "Transportation of small objects by robotic throwing
and catching: applying genetic programming for
trajectory estimation",
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journal = "IFAC-PapersOnLine",
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volume = "51",
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number = "30",
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pages = "533--537",
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year = "2018",
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note = "18th IFAC Conference on Technology, Culture and
International Stability TECIS 2018",
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keywords = "genetic algorithms, genetic programming, GPU, robotic
catching, forecasting, machine vision, machine
learning, CUDA, parallel computing",
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ISSN = "2405-8963",
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DOI = "doi:10.1016/j.ifacol.2018.11.271",
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URL = "http://www.sciencedirect.com/science/article/pii/S2405896318329446",
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abstract = "Robotic catching of thrown objects is one of the
common robotic tasks, which is explored in several
works. This task includes subtask of tracking and
forecasting the trajectory of the thrown object. Here
we propose an algorithm for estimating future
trajectory based on video signal from two cameras. Most
of existing implementations use deterministic
trajectory prediction and several are based on machine
learning. We propose a combined forecasting algorithm
where the deterministic motion model for each
trajectory is generated via the genetic programming
algorithm. Genetic programming is implemented on C++
with use of CUDA library and executed in parallel way
on the graphical processing unit. Parallel execution
allow genetic programming in real time. Numerical
experiments with real trajectories of the thrown tennis
ball show that the algorithm can forecast the
trajectory accurately",
- }
Genetic Programming entries for
Ruslan Gayanov
Konstantin Mironov
Ramil Mukhametshin
Aleksandr Vokhmintsev
Dmitriy Kurennov
Citations