Grasping Optimization in a Three Fingers Final Effector

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This paper presents the optimization of gripping points of an end effector of three fingers, and this is done by ensuring that the force exerted on the object is minimum. It begins with the design of the gripper has two degrees of freedom (DOF) for each finger. Is performed a brief mathematical description of the kinematics involved in the gripper and with this is determined the work area. With the workspace is determines the points with contact with the object geometry are obtained and these are gripping the possible points of the object. To select which of these points is the best to grab the object, we proceed to evaluate the force exerted on the object by means of the mathematic denominated of Screw. This force should be minimal, avoiding sliding and in turn damage the object. As the contact points are numerous and the evaluating would take quite some time in an algorithm combinational by this reason the optimization algorithm Non-dominated Sorting Genetic Algorithm (NSGA) is implemented.

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919-922

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January 2015

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[1] N. Curtis and J. Xiao, Efficient and effective grasping of novel objects through learning and adapting a knowledge base, IEEE - RSJ International Conference on Intelligent Robots and Systems, p.2252–2257, (2008).

DOI: 10.1109/iros.2008.4651062

Google Scholar

[2] Shadow Robot company. Shadow Dexterous Hand Technical Specification, Enero (2013).

Google Scholar

[3] S. Wu and M. Hor, On the properties of 3d equilibrium gmrasping forces, IEEE, p.3101–3106, (1996).

Google Scholar

[4] R. Murray, Z. Li, and S. Sastry, A Mathematical Introduction to Robotic Manipulation,. (1994).

Google Scholar

[5] S. Li and Q. Meng, Grasping force measurement for dynamic grasp stability assess-ment, IEEE, p.1294–1299, (1998).

Google Scholar

[6] F. Cortés, Robótica. Control de Robots Manipuladores. Alfaomega, Marzo (2011).

Google Scholar

[7] M. Mauledoux, V. Shkodyrev, Multiobjective Evolutionary Algorithm MOEA to Solve Task Allocation Problems in Multi Agents Systems, Listed in IEEE Xplore and indexed by both EI (Compendex) and ISI Proceeding (ISTP), 2010, ISBN: 978-1-4244-5585-0, Vol 5.

DOI: 10.1109/iccae.2010.5451885

Google Scholar

[8] M. Mauledoux, V. Shkodyrev, Multiobjective Evolutionary Algorithm MOEA an Approach for Solving MAS Multiatribute Allocation Task, Listed in IEEE Xplore and indexed by both EI (Compendex) and ISI Proceeding (ISTP), 2010 ISBN: 978-1-4244-5585-0, Vol 1.

DOI: 10.1109/iccae.2010.5451953

Google Scholar

[9] A. Coello, An updated survey of evolutionary multiobjective optimization techniques, state of the art and future trends, IEEE Service Center, p.3–13, (1999).

DOI: 10.1109/cec.1999.781901

Google Scholar

[10] A. Coello, G. Lamont, and D. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems,. 2 ed., (2007).

DOI: 10.1007/978-1-4757-5184-0

Google Scholar

[11] K. Deb., Evolutionary algorithms for multi-criterion optimization in engineering design, EURO-GEN 99, (1999).

Google Scholar