Abstract
The shape changes of soft organisms demonstrate the survival rules in the evolution of self life. It is important for the transform control of soft robots about how to envolve suitable for the shape demands. In this paper, the compositional pattern producing networks (CPPN) algorithm was used to evolve soft robot. By taking simple random functions as genotype inputs, the functions can be weighted combinations to generate the desired phenotype, which mapping relationship between genotypes and phenotypes can be achieved. The VoxCAD simulation software was used to build the three-dimensional topological structure of soft robot and the evolution process of the virtual life was realized by using the specific rule shape to simulate the real environment. The evolutionary analysis of the four-legged walking soft robot was carried out in the simulation experiment, which the effectiveness of the method was verified.
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
References
Sanan, S., Moidel, J., Atkeson, C.G.: A continuum approach to safe robots for physical human interaction. In: International Symposium on Quality of Life Technology, June 2011
Turk, M.G.: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (1994)
Cheney, N., MacCurdy, R., Clune, J., Lipson, H.: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. ACM SIGEVOlution 7(1), 11–23 (2014)
Cheney, N., Bongard, J., Lipson, H.: Evolving soft robots in tight spaces. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 935–942. ACM, July 2015
Kriegman, S., Cheney, N., Corucci, F., Bongard, J.C.: A minimal developmental model can increase evolvability in soft robots. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 131–138. ACM, July 2017
Corucci, F.: Evolutionary developmental soft robotics: towards adaptive and intelligent soft machines following nature’s approach to design. In: Laschi, C., Rossiter, J., Iida, F., Cianchetti, M., Margheri, L. (eds.) Soft Robotics: Trends, Applications and Challenges. BIOSYSROB, vol. 17, pp. 111–116. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-46460-2_14
Woolley, B.G., Stanley, K.O.: Evolving a single scalable controller for an octopus arm with a variable number of segments. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN 2010. LNCS, vol. 6239, pp. 270–279. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15871-1_28
Lessin, D., Risi, S.: Soft-body muscles for evolved virtual creatures: the next step on a bio-mimetic path to meaningful morphological complexity. In: Proceedings of the European Conference on Artificial Life 13, pp. 604–611. MIT Press, Cambridge, July 2015
Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program Evolvable Mach. 8(2), 131–162 (2007)
Auerbach, J.E., Bongard, J.C.: Evolving CPPNs to grow three-dimensional physical structures. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 627–634. ACM, July 2010
Cheney, N., Lipson, H.: Topological evolution for embodied cellular automata. Theor. Comput. Sci. 633, 19–27 (2016)
Hiller, J.D., Lipson, H.: Evolving amorphous robots. In: ALIFE, pp. 717–724, August 2010
Arita, T., Joachimczak, M., Ito, T., Asakura, A., Suzuki, R.: ALife approach to eco-evo-devo using evolution of virtual creatures. Artif. Life Robot. 21(2), 141–148 (2016)
Stanley, K.O., D’Ambrosio, D.B., Gauci, J.: A hypercube-based encoding for evolving large-scale neural networks. Artif. Life 15(2), 185–212 (2009)
Cubes, M.: A high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, pp. 163–169. Association for Computing Machinery, New York, July 1987
Clune, J., Lipson, H.: Evolving three-dimensional objects with a generative encoding inspired by developmental biology. In: ECAL, pp. 141–148, June 2011
Acknowledgement
This work reported here was supported by the Key Projects of Science and Technology Plan of Zhejiang Province (Grant No. 2019C04018).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gu, Y. et al. (2019). Research on Motion Evolution of Soft Robot Based on VoxCAD. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-27535-8_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27534-1
Online ISBN: 978-3-030-27535-8
eBook Packages: Computer ScienceComputer Science (R0)