Abstract
This paper presents an evolutionary approach to the Inverse Kinematics problem. The Inverse Kinematics problem concerns finding the placement of a manipulator that satisfies certain conditions. In this paper apart from reaching the target point the manipulator is required to avoid a number of obstacles. The problem which we tackle is dynamic: the obstacles and the target point may be moving which necessitates the continuous update of the solution. The evolutionary algorithm used for this task is a modification of the Infeasibility Driven Evolutionary Algorithm (IDEA) augmented with a prediction mechanism based on the ARIMA model.
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
Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72(13-15), 2729–2730 (2009)
Balestrino, A., De Maria, G., Sciavicco, L.: Robust control of robotic manipulators. In: Proceedings of the 9th IFAC World Congress, vol. 5, pp. 2435–2440 (1984)
Bertram, D., Kuffner, J., Dillmann, R., Asfour, T.: An Integrated Approach to Inverse Kinematics and Path Planning for Redundant Manipulators. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1874–1879 (2006)
Box, G.E.P., Jenkins, G.M.: Time series analysis: Forecasting and control, revised edition. Holden-Day, San Francisco (1976)
Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)
Corchado, E., Graña, M., Wozniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)
Fêdor, M.: Application of inverse kinematics for skeleton manipulation in real-time. In: Proceedings of the 19th Spring Conference on Computer Graphics, pp. 203–212. ACM (2003)
Garca, S., Fernndez, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180(10), 2044–2064 (2010)
Goldenberg, A.A., Benhabib, B., Fenton, G.: A complete generalized solution to the inverse kinematics of robots. IEEE Journal of Robotics and Automation RA-1(1) (1985)
Goldenberg, A.A., Lawrence, D.L.: A generalized solution to the inverse kinematics of robot manipulators. ASME Journal of Dynamic Systems, Measurement, and Control 107, 103–106 (1985)
Hatzakis, I., Wallace, D.: Dynamic multi-objective optimization with evolutionary algorithms: a forward-looking approach. In: Proceedings of the GECCO 2006, pp. 1201–1208. ACM (2006)
Karla, P., Mahapatra, P.B., Aggarwal, D.K.: On the solution of multimodal robot inverse kinematics function using real-coded genetic algorithms. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 2, pp. 1840–1845 (2003)
Karla, P., Mahapatra, P.B., Aggarwal, D.K.: On the comparison of niching strategies for finding the solution of multimodal robot inverse kinematics. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, vol. 6, pp. 5356–5361 (2004)
Pedrycz, W., Aliev, R.: Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73(1-3), 10–23 (2009)
Singh, H.K., Isaacs, A., Tapabrata, R.: Infeasibility Driven Evolutionary Algorithm (IDEA) for engineering design optimization. In: Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence, pp. 104–115 (2008)
Singh, H.K., Isaacs, A., Nguyen, T.T., Ray, T., Yao, X.: Performance of infeasibility driven evolutionary algorithm (IDEA) on constrained dynamic single objective optimization problems. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC 2009, pp. 3127–3134 (2009)
Tabandeh, S., Clark, C., Melek, W.: A genetic algorithm approach to solve for multiple solutions of inverse kinematics using adaptive niching and clustering. In: Proceedings of IEEE Congress on Evolutionary Computation, CEC 2006, pp. 1815–1822 (2006)
Wilfong, G.: Motion planning in the presence of movable obstacles. In: Proceedings of ACM Symposium on Computational Geometry, pp. 279–288 (1988)
Wolovich, W.A., Elliot, H.: A computational technique for inverse kinematics. In: Proceedings of 23rd IEEE Conference on Decision and Control, pp. 1359–1363 (1984)
Zhou, A., Jin, Y., Zhang, Q., Sendhoff, B., Tsang, E.: Prediction-Based Population Re-initialization for Evolutionary Dynamic Multi-objective Optimization. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 832–846. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Filipiak, P., Michalak, K., Lipinski, P. (2012). A Predictive Evolutionary Algorithm for Dynamic Constrained Inverse Kinematics Problems. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_55
Download citation
DOI: https://doi.org/10.1007/978-3-642-28942-2_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28941-5
Online ISBN: 978-3-642-28942-2
eBook Packages: Computer ScienceComputer Science (R0)