Advertisement

Evolution of Locomotion Gaits for Quadrupedal Robots and Reality Gap Characterization

  • Usama MirEmail author
  • Zainullah Khan
  • Umer Iftikhar Mir
  • Farhat Naseer
  • Waleed Shah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11934)

Abstract

The landscape of isolated areas has been changed due to human intervention to support vehicular transport, however, this is a hectic job, therefore, if our vehicles are morphed to mimic nature, the landscape would not need to be changed. Robots and vehicles inspired from nature are very hard to control because of multiple number of actuators. Manual methods (such as programming individual actuators to form a walking pattern) fall short because of the complexity. Therefore, an automated process that employs artificial intelligence (AI) to evolve locomotion gaits for quadrupedal robots is needed. AI has been used before as well; however, most of the AI implementations are only done in simulation without hardware execution. This article attempts to use genetic algorithms to evolve locomotion gaits that are later implemented on robots both via simulations and real implementation. The simulation is run for 200 generations and the best result is put into effect on a hardware robot. Our results show that the gait is successfully transferred; however, the results are not perfect and suffer from the reality gap. These results also help us conclude that gaits designed for a specific environment have a better chance of transferring than gaits that have been designed without taking into account the surface the robot walks on.

Keywords

Evolutionary robotics Gait evolution Genetic algorithm 

References

  1. 1.
    Pfeifer, R., Bongard, J.C.: How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press (2006)Google Scholar
  2. 2.
    Bongard, J.C.: Evolutionary robotics, evolutionary robotics. Commun. ACM 56, 74–83 (2013)CrossRefGoogle Scholar
  3. 3.
    Pongas, D., Mistry, M., Schaal, S.: A robust quadruped walking gait for traversing rough terrain. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 1474–1479. IEEE, Roma (2007).  https://doi.org/10.1109/ROBOT.2007.363192
  4. 4.
    Hauert, S., Zufferey, J.C., Floreano, D.: Reverse-engineering of artificially evolved controllers for swarms of robots. In: 2009 Congress on Evolutionary Computation CEC 2009, pp. 55–61 (2009).  https://doi.org/10.1109/CEC.2009.4982930
  5. 5.
    Juang, C.F., Yeh, Y.T.: Multiobjective evolution of biped robot gaits using advanced continuous ant-colony optimized recurrent neural networks. IEEE Trans. Cybern. 1–13 (2017).  https://doi.org/10.1109/TCYB.2017.2718037CrossRefGoogle Scholar
  6. 6.
    Masood, J., Samad, A., Abbas, Z., Khan, L.: Evolution of locomotion controllers for snake robots. In: 2016 2nd International Conference on Robotics and Artificial Intelligence, ICRAI 2016, pp. 164–169 (2016).  https://doi.org/10.1109/ICRAI.2016.7791247
  7. 7.
    Reil, T., Husbands, P.: Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Trans. Evol. Comput. 6, 159–168 (2002).  https://doi.org/10.1109/4235.996015CrossRefGoogle Scholar
  8. 8.
    Yosinski, J., Clune, J., Hidalgo, D., Nguyen, S., Zagal, J.C., Lipson, H.: Evolving robot gaits in hardware: the HyperNEAT generative encoding vs. parameter optimization. In: Proceedings of European Conference on Artificial Life, pp. 1–8 (2011)Google Scholar
  9. 9.
    Nygaard, T.F., Torresen, J., Glette, K.: Multi-objective evolution of fast and stable gaits on a physical quadruped robotic platform. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2017).  https://doi.org/10.1109/SSCI.2016.7850167
  10. 10.
    Glette, K., Klaus, G., Zagal, J., Torresen, J.: Evolution of locomotion in a simulated quadruped robot and transferral to reality. In: Proceedings of the Seventeenth International Symposium on Artificial Life and Robotics, pp. 1–4 (2012)Google Scholar
  11. 11.
    Sofge, D.A., Potter, M.A., Bugajska, M.D., Schultz, A.C.: Challenges and opportunities of evolutionary robotics. Robotics (2003)Google Scholar
  12. 12.
    Juang, C.F., Chen, Y.H., Jhan, Y.H.: Wall-following control of a hexapod robot using a data-driven fuzzy controller learned through differential evolution. IEEE Trans. Ind. Electron. 62, 611–619 (2015).  https://doi.org/10.1109/TIE.2014.2319213CrossRefGoogle Scholar
  13. 13.
    Phillips, A., Du Plessis, M.: Towards the incorporation of proprioception in evolutionary robotics controllers. In: Proceedings - 3rd International Conference on Robotic Computing IRC 2019. 226–229 (2019).  https://doi.org/10.1109/IRC.2019.00041
  14. 14.
    D’Ausilio, A.: Arduino: a low-cost multipurpose lab equipment. Behav. Res. Methods 44, 305–313 (2012).  https://doi.org/10.3758/s13428-011-0163-zCrossRefGoogle Scholar
  15. 15.
    Eckert, P., Ijspeert, A.J.: Benchmarking agility for multilegged terrestrial robots. IEEE Trans. Robot. 35, 529–535 (2019).  https://doi.org/10.1109/TRO.2018.2888977CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Usama Mir
    • 1
    Email author
  • Zainullah Khan
    • 2
  • Umer Iftikhar Mir
    • 2
  • Farhat Naseer
    • 2
  • Waleed Shah
    • 2
  1. 1.Saudi Electronic UniversityDammamSaudi Arabia
  2. 2.Engineering and Management SciencesBalochistan University of Information TechnologyQuettaPakistan

Personalised recommendations