Abstract
In this paper a multiobjective dynamic constrained evolutionary algorithm is proposed for control of a multi-segment articulated manipulator. The algorithm is tested in simulated dynamic environments with moving obstacles. The algorithm does not require previous training - a feasible sequence of movements is found and maintained based on a population of candidate movements. The population is evolved using typical evolutionary operators as well as several new ones that are dedicated for the manipulator control task. The algorithm is shown to handle manipulators with up to 100 segments. The increased maneuverability of the manipulator with 100 segments is well utilized by the algorithm. The results obtained for such manipulator are better than for the 10-segment one which is computationally easier to handle.
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References
Aristidou, A., Lasenby, J.: Motion capture with constrained inverse kinematics for real-time hand tracking. In: Proceedings of the IEEE International Symposium on Communications, Control and Signal Processing (ISCCSP 2010), pp. 1–5 (2010)
Deb, K., Agarwal, R.: Simulated binary crossover for continuous search space. Complex Systems 9(2), 115–148 (1995)
Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Deb, K., Goyal, M.: A combined genetic adaptive search (GeneAS) for engineering design. Computer Science and Informatics 26, 30–45 (1996)
Dong, H., et al.: Workspace density and inverse kinematics for planar serial revolute manipulators. Mechanism and Machine Theory 70, 508–522 (2013)
Duindam, V., Xu, J., Alterovitz, R., Sastry, S., Goldberg, K.: Three-dimensional motion planning algorithms for steerable needles using inverse kinematics. The International Journal of Robotics Research 29(7), 789–800 (2010)
Grefenstette, J.: Genetic algorithms for changing environments. In: Parallel Problem Solving from Nature 2, pp. 137–144. Elsevier (1992)
Kallmann, M.: Analytical inverse kinematics with body posture control. Computer Animation and Virtual Worlds 19(2), 79–91 (2008)
Pieper, D.L.: The kinematics of robots under computer control. Phd thesis, Stanford University (1968)
Singh, H.K., Isaacs, A., Ray, T., Smith, W.: Infeasibility driven evolutionary algorithm (IDEA) for engineering design optimization. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 104–115. Springer, Heidelberg (2008)
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Michalak, K., Filipiak, P., Lipinski, P. (2014). Multiobjective Dynamic Constrained Evolutionary Algorithm for Control of a Multi-segment Articulated Manipulator. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_25
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DOI: https://doi.org/10.1007/978-3-319-10840-7_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10839-1
Online ISBN: 978-3-319-10840-7
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