Application of the “Alliance Algorithm” to Energy Constrained Gait Optimization

  • Valerio Lattarulo
  • Sander G. van Dijk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7416)


This paper deals with the problem of energy constrained gait optimization for bipedal walking. We present a solution to this problem obtained by applying a recently introduced heuristic method, the Alliance Algorithm (AA), and compare its performance against a Genetic Algorithm (GA). We show experimentally that the intrinsic ability of the AA to handle hard constraints enables it to find solutions significantly better than the GA. Also with the constraint removed the AA show more reliable optimization results. Finally, we show that the final gait obtained through this method outperforms most solutions to this problem presented in previous works, in terms of walking speed.


Genetic Algorithm Particle Swarm Optimization Solution Space Central Pattern Generator Hard Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Calderaro, V., Galdi, V., Lattarulo, V., Siano, P.: A new algorithm for steady state load-shedding strategy. In: 12th International Conference on Optimization of Electrical and Electronic Equipment (OPTIM), pp. 48–53 (2010)Google Scholar
  2. 2.
    Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: European Conference on Artificial Life, pp. 134–142 (1991)Google Scholar
  3. 3.
    Goswami, A., Kallem, V.: Rate of change of angular momentum and balance maintenance of biped robots. In: ICRA, pp. 3785–3790 (2004)Google Scholar
  4. 4.
    Holland, J.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. MIT Press, Cambridge (1992)Google Scholar
  5. 5.
    IJspeert, A.J.: Central pattern generators for locomotion control in animals and robots: A review. Neural Networks 21(4), 642–653 (2008)CrossRefGoogle Scholar
  6. 6.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (August 1995)Google Scholar
  7. 7.
    Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Morimoto, J., Endo, G., Nakanishi, J., Cheng, G.: A biologically inspired biped locomotion strategy for humanoid robots: Modulation of sinusoidal patterns by a coupled oscillator model. IEEE Transactions on Robotics 24(1), 185–191 (2008)CrossRefGoogle Scholar
  9. 9.
    Obst, O., Rollmann, M.: SPARK – A Generic Simulator for Physical Multiagent Simulations. Computer Systems Science and Engineering 20(5), 347–356 (2005)Google Scholar
  10. 10.
    Shafii, N., Aslani, S., Nezami, O.M., Shiry, S.: Evolution of Biped Walking Using Truncated Fourier Series and Particle Swarm Optimization. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009. LNCS (LNAI), vol. 5949, pp. 344–354. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Shafii, N., Reis, L.P., Lau, N.: Biped Walking Using Coronal and Sagittal Movements Based on Truncated Fourier Series. In: Ruiz-del-Solar, J., Chown, E., Ploeger, P.G. (eds.) RoboCup 2010. LNCS (LNAI), vol. 6556, pp. 324–335. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evolutionary Computation 10(2), 99–127 (2002)CrossRefGoogle Scholar
  13. 13.
    Vukobratovic, M., Borovac, B.: Zero-moment point-thirty five years of its life. International Journal of Humanoid Robotics 1(1), 157–173 (2004)CrossRefGoogle Scholar
  14. 14.
    Yang, L., Chew, C., Poo, A., Zielinska, T.: Adjustable bipedal gait generation using genetic algorithm optimized fourier series formulation. In: IROS, pp. 4435–4440. IEEE (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Valerio Lattarulo
    • 1
  • Sander G. van Dijk
    • 1
  1. 1.Adaptive Systems Research GroupUniversity of HertfordshireHatfieldUK

Personalised recommendations