An Intelligent Inference System for Robot Hand Optimal Grasp Preshaping

Download

index.pdf

Date

2010-10-01

Author

BAYSAL, CABBAR VEYSEL
Erkmen, Aydan Müşerref

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

136
views

0
downloads

This paper presents a novel Intelligent Inference System (IIS) for the determination of an optimum preshape for multifingered robot hand grasping, given object under a manipulation task. The IIS is formed as hybrid agent architecture, by the synthesis of object properties, manipulation task characteristics, grasp space partitioning, low-level kinematical analysis, evaluation of contact wrench patterns via fuzzy approximate reasoning and ANN structure for incremental learning. The IIS is implemented in software with a robot hand simulation.

Subject Keywords

General Computer Science, Computational Mathematics

URI

https://hdl.handle.net/11511/36782

Journal

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS

DOI

https://doi.org/10.1080/18756891.2010.9727731

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

A Favorable Weight-Based Evolutionary Algorithm for Multiple Criteria Problems SOYLU, Banu; Köksalan, Mustafa Murat (Institute of Electrical and Electronics Engineers (IEEE), 2010-04-01) In this paper, we present a favorable weight-based evolutionary algorithm for multiple criteria problems. The algorithm tries to both approximate the Pareto frontier and evenly distribute the solutions over the frontier. These two goals are common for many multiobjective evolutionary algorithms. To achieve these goals in our algorithm, each member selects its own weights for a weighted Tchebycheff distance function to define its fitness score. The fitness scores favor solutions that are closer to the Pareto...
A Cascadable Random Neural Network Chip with Reconfigurable Topology Badaroglu, Mustafa; Halıcı, Uğur; Aybay, Isik; Cerkez, Cuneyt (Oxford University Press (OUP), 2010-03-01) A digital integrated circuit (IC) is realized using the random neural network (RNN) model introduced by Gelenbe. The RNN IC employs both configurable routing and random signaling. In this paper we present the networking/routing aspects as well as the performance results of an RNN network implemented by the RNN IC. In the RNN model, each neuron accumulates arriving signals and can fire if its potential at a given instant of time is strictly positive. Firing occurs at random, the intervals between successive ...
A temporal neural network model for constructing connectionist expert system knowledge bases Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01) This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
A finite field framework for modeling, analysis and control of finite state automata Reger, Johann; Schmidt, Klaus Verner (Informa UK Limited, 2004-09-01) In this paper, we address the modeling, analysis and control of finite state automata, which represent a standard class of discrete event systems. As opposed to graph theoretical methods, we consider an algebraic framework that resides on the finite field F-2 which is defined on a set of two elements with the operations addition and multiplication, both carried out modulo 2. The key characteristic of the model is its functional completeness in the sense that it is capable of describing most of the finite st...
A nested iterative scheme for computation of incompressible flows in long domains Manguoğlu, Murat; Tezduyar, Tayfun E.; Sathe, Sunil (Springer Science and Business Media LLC, 2008-12-01) We present an effective preconditioning technique for solving the nonsymmetric linear systems encountered in computation of incompressible flows in long domains. The application category we focus on is arterial fluid mechanics. These linear systems are solved using a nested iterative scheme with an outer Richardson scheme and an inner iteration that is handled via a Krylov subspace method. Test computations that demonstrate the robustness of our nested scheme are presented.

Citation Formats

C. V. BAYSAL and A. M. Erkmen, “An Intelligent Inference System for Robot Hand Optimal Grasp Preshaping,” INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS, pp. 656–673, 2010, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36782.