Modelling and simulation of 3DOF parallel manipulator using artificial neural network

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dc.AffiliationOctober University for modern sciences and Arts (MSA)
dc.contributor.authorYoussef A.
dc.contributor.authorBayoumy A.M.
dc.contributor.authorRostom M.
dc.contributor.authorTolbah F.A.
dc.contributor.otherT.A at Mechatronics Dept.
dc.contributor.otherFaculty of Engineering
dc.contributor.otherMUST
dc.contributor.otherGiza
dc.contributor.otherEgypt; Mechatronics Dept.
dc.contributor.otherFaculty of Engineering
dc.contributor.otherMSA
dc.contributor.otherGiza
dc.contributor.otherEgypt; Mechatronics Dept.
dc.contributor.otherFaculty of Engineering
dc.contributor.otherAASMT
dc.contributor.otherCairo
dc.contributor.otherEgypt; Mechatronics Dept.
dc.contributor.otherFaculty of Engineering
dc.contributor.otherASU
dc.contributor.otherCairo
dc.contributor.otherEgypt
dc.date.accessioned2020-01-09T20:40:32Z
dc.date.available2020-01-09T20:40:32Z
dc.date.issued2019
dc.descriptionScopus
dc.description.abstractParallel Robot (PR) has shown its ability to be precise in its movement. Actuators move simultaneously to achieve the required target, on top of that its payload is much greater than what a serial robot can withstand. To determine workspace of the robot with known angles Forward kinematics has to be introduced which, bring a lot of difficulty as it requires the solution of multiple coupled nonlinear algebraic equations. Those equations bring multiple valid solutions. Those solutions could lead to different locations. As it is not going to make the pick and place for PR will be easier. This paper will discuss a numerical method that calculates the Forward Kinematics for PR. This method uses Artificial Neural Network which relay on training with a certain number of iterations. The set of data to be used in the training can be obtained from PR simulation. This method will serve to know workspace around PR as it will help it to pick the target object. � 2019 Published under licence by IOP Publishing Ltd.en_US
dc.description.sponsorshipEgyptian Ministry of Defenseen_US
dc.identifier.doihttps://doi.org/10.1088/1757-899X/610/1/012080
dc.identifier.doiPubMed ID :
dc.identifier.issn17578981
dc.identifier.otherhttps://doi.org/10.1088/1757-899X/610/1/012080
dc.identifier.otherPubMed ID :
dc.identifier.urihttps://t.ly/eRZB3
dc.language.isoEnglishen_US
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofseriesIOP Conference Series: Materials Science and Engineering
dc.relation.ispartofseries610
dc.subjectAerospace engineeringen_US
dc.subjectKinematicsen_US
dc.subjectManipulatorsen_US
dc.subjectNeural networksen_US
dc.subjectNumerical methodsen_US
dc.subjectRobotsen_US
dc.subjectForward kinematicsen_US
dc.subjectModelling and simulationsen_US
dc.subjectNonlinear algebraic equationsen_US
dc.subjectNumber of iterationsen_US
dc.subjectParallel manipulatorsen_US
dc.subjectParallel robotsen_US
dc.subjectPick and placeen_US
dc.subjectSerial robotsen_US
dc.subjectNonlinear equationsen_US
dc.titleModelling and simulation of 3DOF parallel manipulator using artificial neural networken_US
dc.typeConference Paperen_US
dcterms.isReferencedByPrempraneerach, P., Editor Delta parallel robot workspace and dynamic trajectory tracking of delta parallel robot (2014) International Computer Science and Engineering Conference (ICSEC), p. 2014; �, B., Hwang, M.J., Cavusoglu, M.C., Design of a Parallel Robot for Needle-Based Interventions on Small Animals (2013) IEEE/ASME Transactions on Mechatronics, 18 (1), pp. 62-73; Muis, A., Ohnishi, K., Eye-to-hand approach on eye-in-hand configuration within real-time visual servoing (2005) IEEE/ASME Transactions on Mechatronics, 10 (4), pp. 404-410; Luo, R.C., Chou, S., Yang, X., Peng, N., Editors. Hybrid Eye-to-hand and Eye-in-hand visual servo system for parallel robot conveyor object tracking and fetching (2014) IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society, p. 2558; Abramov, A., Pauwels, K., Papon, J., W�rg�tter, F., Dellen, B., (2012) IEEE Workshop on the Applications of Computer Vision (WACV); Merlet, J.-P., (2000) Robotics Research, pp. 27-32; Mustafa, M., Misuari, R., Daniyal, H., Editors. Forward Kinematics of 3 Degree of Freedom Delta Robot (2007) 5th Student Conference on Research and Development; Dehghani, M., Ahmadi, M., Khayatian, A., Eghtesad, M., Farid, M., Neural network solution for forward kinematics problem of HEXA parallel robot (2008) American Control Conference 2008, pp. 4214-4219; Tsai, C.-S., Yao, A., Radakovic, N., Wei, H.-Y., Zhong, C.-Y., Zhou, Z.-J., Design and Simulation of a Delta Type Robot (2016) International Symposium on Computer, Consumer and Control (IS3C)2016, pp. 370-373; Robot, A.F.D., (2016) The Delta Parallel Robot: Kinematics Solutions Robert L. Williams II, Ph. D.; Chen, S.H., Jakeman, A.J., Norton, J.P., Artificial Intelligence techniques: An introduction to their use for modelling environmental systems (2008) Mathematics and Computers in Simulation, 78 (2-3), pp. 379-400; Solid works 2017 online documentation
dcterms.sourceScopus

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