Abstract
Several models of computational intelligence have been proven to be useful in robotic devices control. This work evaluates three models to solve the inverse kinematics problem of a robotic manipulator with two degrees of freedom, which is used to position objects in conjunction with the CoroBot platform. They were initially developed the direct and inverse kinematic models using the homogeneous transformation matrices, extracting from these, the training, testing and validation data used in the three models. This paper takes advantage of the great potential of artificial neural networks, in order to determine the feasibility and response of the experiments performed, keeping in focus the possible applications and modification of design and training parameters. The models used are Feedforward Neural Networks, Neuro-Fuzzy Systems and finally Echo State Networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tondu, B.: Kinematic modelling of anthropomorphic robot upper limb with human-like hands. In: IEEE International Conference on Advanced Robotics, pp. 1–9 (2009)
Adhyaru, D., Patel, J., Gianchandani, R.: Adaptive Neuro-Fuzzy Inference System based control of robotic manipulators. In: IEEE International Conference on Mechanical and Electrical Technology, pp. 353–358 (2010)
Young, H., Lewis, F., Dawson, D.: Intelligent optimal control of robotic manipulator using neural networks. Automatica 36(9), 1355–1364 (2000)
Bhambhani, B., Valbuena-Reyes, L., Tanner, H.: Spatially distributed cellular neural networks. International Journal of Intelligent Computing and Cybernetics 4(4), 465–486 (2011)
Cheng, K., Dasgupta, P.: Multi-Agent coalition formation for distributed area coverage: analysis and evaluation. In: IEEE International Conference on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 334–337 (2010)
Jaeger, H.: The “ echo state ” approach to analysing and training recurrent neural networks – with an Erratum note 1. GMD Report (148), 1–47 (2010)
Xi, J., Shi, Z., Han, M.: Analyzing the state space property of echo state networks for chaotic system prediction. In: IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1412–1417 (2005)
Bian, X., Mou, C.: Identification of non-linear dynamic model of UUV based on ESN neural network. In: IEEE 30th Chinese Control Conference, pp. 1432–1437 (2011)
Ni, S., et al.: Predictive Control of Vehicle Based on Echo State Network. In: IEEE International Conference on Intelligence Science and Information Engineering, pp. 37–40 (2011)
Jaeger, H.: Reservoir riddles: suggestions for echo state network research (extended abstract). In: IEEE International Joint Conference on Neural Networks, vol. 3, pp. 1460–1462 (2005)
Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)
Ganesh, K., Shishir, B.: Effects of spectral radius and settling time in the performance of echo state networks. Neural Networks 22(7), 861–863 (2009)
Aghajarian, M., Kiani, K.: Inverse kinematics solution of PUMA 560 robot arm using ANFIS. In: IEEE International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 574–578 (2011)
Yang, M., Lu, G., Li, J.: An inverse kinematics solution for manipulators based on fuzzy. In: IEEE International Conferences on Info-tech and Info-net, vol. 4, pp. 400–404 (2001)
Pham, D., Castellani, M., Fahmy, A.: Learning the inverse kinematics of a robot manipulator using the Bees Algorithm. In: IEEE International Conference on Industrial Informatics, pp. 493–498 (2008)
Wang, L., Chai, T., Yang, L.: Neural-Network-Based Contouring Control for Robotic Manipulators in Operational Space. IEEE Transactions on Control Systems Technology (99), 1–8 (2011)
Wei, L.-X., Wang, H.-R., Li, Y.: A new solution for inverse kinematics of manipulators based on neural network. In: IEEE International Conference on Machine Learning and Cybernetics, vol. 2, pp. 1201–1203 (2003)
Xue, Y., Yang, L.: Development of a new recurrent neural network toolbox (RNTool). A Course report on training recurrent multilayer perceptron and echo statenetwork (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Valencia, C.H., Vellasco, M.M.B.R., Figueiredo, K.T. (2013). Comparative Analysis of Arm Control Performance Using Computational Intelligence. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-37374-9_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37373-2
Online ISBN: 978-3-642-37374-9
eBook Packages: EngineeringEngineering (R0)