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Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm

Published: 06 July 2018 Publication History

Abstract

This paper proposes a new recurrent neural network (RNN) structure evolved to control the gait of a hexapod robot for fast forward walking. In this evolutionary robot, the gait control problem is formulated as an optimization problem with the objective of a fast forward walking speed and a small deviation in the forward walking direction. Evolutionary optimization of the RNNs through a group-based hybrid metaheuristic algorithm is proposed to find the optimal RNN controller. Preliminary simulation results with comparisons show the advantage of the proposed approach1.

References

[1]
S. Nolfi and D. Floreano. 2001. Evolutionary Robotics - The Biology, Intelligence, and Technology of Self-Organizing Machines. London, UK.: MIT Press, (2001).
[2]
J. Yu, M. Tan, J. Chen, and J. Zhang. 2014. A survey on CPG-inspired control models and system implementation. IEEE Trans. Neural Networks and Learning Systems. 25, 3 (2014), 441--456.
[3]
J. Santos and A. Campo. 2012. Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks. Neurocomputing. 86, 1 (2012), 86--96.
[4]
C. F. Juang and Y. T. Yeh. 2018. Multi-objective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks. IEEE Trans. Cyber. In Press (2018).
[5]
R. D. Beer and J. C. Gallagher. 1992. Evolving dynamical neural networks for adaptive behavior. Adapt. Behavior. 1, 1 (1992), 92--122.
[6]
C. F. Juang, Y. C. Chang, and C. M. Hsiao. 2011. Evolving gaits of a hexapod robot by recurrent neural networks with symbiotic species-based particle swarm optimization. IEEE Trans. Industrial Electronics. 58, 7 (2011), 3110--3119.
[7]
C. F. Juang. 2004. A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst., Man, and Cyber., Part B: Cyber. 34, 2 (2004), 997--1006.
[8]
S. Jeong, S. Hasegawa, K. Shimoyama, and S. Obayashi. 2009. Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real world design optimization. IEEE Comput. Intell. Mag. 4, 3 (2009), 36--44.
[9]
C. F. Juang and Y. C. Chang. 2011. Evolutionary group-based particle swarm optimized fuzzy controller with application to mobile robot navigation in unknown environments. IEEE Trans. Fuzzy Syst. 19, 2 (2011), 379--392. IEEE Trans. Neural Networks and Learning Systems. 25, 3 (2014), 441--456.

Cited By

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  • (2020)Control Strategy of Mobile Robots Using Fuzzy-Gravitational Search Method and Review of Other TechniquesInnovative Product Design and Intelligent Manufacturing Systems10.1007/978-981-15-2696-1_55(565-577)Online publication date: 14-Mar-2020
  • (2020)Navigational Control Analysis of Mobile Robot in Cluttered Unknown Environment Using Novel Neural-GSA TechniqueInnovative Product Design and Intelligent Manufacturing Systems10.1007/978-981-15-2696-1_54(551-563)Online publication date: 14-Mar-2020

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  1. Evolutionary hexapod robot gait control using a new recurrent neural network learned through group-based hybrid metaheuristic algorithm

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        cover image ACM Conferences
        GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion
        July 2018
        1968 pages
        ISBN:9781450357647
        DOI:10.1145/3205651
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Published: 06 July 2018

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        Author Tags

        1. evolutionary robots
        2. genetic algorithms
        3. hexapod robots
        4. particle swarm optimization

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        • (2020)Control Strategy of Mobile Robots Using Fuzzy-Gravitational Search Method and Review of Other TechniquesInnovative Product Design and Intelligent Manufacturing Systems10.1007/978-981-15-2696-1_55(565-577)Online publication date: 14-Mar-2020
        • (2020)Navigational Control Analysis of Mobile Robot in Cluttered Unknown Environment Using Novel Neural-GSA TechniqueInnovative Product Design and Intelligent Manufacturing Systems10.1007/978-981-15-2696-1_54(551-563)Online publication date: 14-Mar-2020

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