Analyzing of flexible gripper by computational intelligence approach
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- @Article{Petkovic:2016:Mechatronics,
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author = "Dalibor Petkovic and Srdjan Jovic and Obrad Anicic and
Bogdan Nedic and Branko Pejovic",
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title = "Analyzing of flexible gripper by computational
intelligence approach",
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journal = "Mechatronics",
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volume = "40",
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pages = "1--16",
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year = "2016",
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ISSN = "0957-4158",
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DOI = "doi:10.1016/j.mechatronics.2016.09.001",
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URL = "http://www.sciencedirect.com/science/article/pii/S0957415816300940",
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abstract = "Adaptive grippers should be able to detect and
recognize grasping objects. To be able to do it control
algorithm need to be established to control gripper
tasks. Compliant underactuated mechanisms with passive
behavior can be used for modelling of adaptive robotic
fingers. Undearactuation is a feature which allows
fully adaptability of robotic fingers for different
objects. In this study gripper with two fingers was
established. Finite element method (FEM) procedure was
used to optimize the gripper structural topology.
Kinetostatic model of the underactuated finger
mechanism was analyzed. This design of the gripper has
embedded sensors as part of its structure. The use of
embedded sensors in a robot gripper gives the control
system the ability to control input displacement of the
gripper and to recognize specific shapes of the
grasping objects. Since the conventional control
strategy is a very challenging task, soft computing
based controllers are considered as potential
candidates for such an application. The sensors could
be used for grasping shape detection. Given that the
contact forces of the finger depend on contact position
of the finger and object, it is suitable to make a
prediction model for the contact forces in function of
contact positions of the finger and grasping objects.
The prediction of the contact forces was established by
using a soft computing (computational intelligence)
approach. To perform the contact forces estimation
adaptive neuro-fuzzy (ANFIS) methodology was used. FEM
simulations were performed in order to acquire
experimental data for ANFIS training. The main goal was
to apply ANFIS network in order to find correlation
between sensors' stresses and finger contact forces.
Afterwards ANFIS results were compared with benchmark
models (extreme learning machine (ELM), extreme
learning machine with discrete wavelet algorithm
(ELM-WAVELET), support vector machines (SVM), support
vector machines with discrete wavelet algorithm
(SVM-WAVELET), genetic programming (GP) and artificial
neural network (ANN)). The reliability of these
computational models was analyzed based on simulation
results.",
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keywords = "genetic algorithms, genetic programming, Compliant
gripper, Adaptive gripper, Underactuation, Contact
forces, ANFIS",
- }
Genetic Programming entries for
Dalibor Petkovic
Srdan Jovic
Obrad Anicic
Bogdan Nedic
Branko Pejovic
Citations