The objective of this experiments is to develop feasible gait patterns that could be used to control a real hexapod walking robot. These gaits should enable the fastest movement that is possible with the given robot's mechanics and drives on a flat terrain. Biological inspirations are commonly used in the design of walking robots and their control algorithms. However, legged robots differ significantly from their biological counterparts. Hence we believe that gait patterns should be learned using the robot or its simulation model rather than copied from insect behaviour. However, as we have found tahula rasa learning ineffective in this case due to the large and complicated search space, we adopt a different strategy: in a series of simulations we show how a progressive reduction of the permissible search space for the leg movements leads to the evolution of effective gait patterns.


  1. D. Belter, A. Kasinski, P. Skrzypczynski, Evolving Feasible Gaits for a Hexapod Robot by Reducing the Space of Possible Solutions, IEEE/RSJ 2008 International Conference on Intelligent Robots and Systems, Nice, France, September 22-26, pp. 2673-2678, 2008 (pdf)

  2. D. Belter, P. Skrzypczyński, Population Based Methods for Identification and Optimization of a Walking robot Model, Lectures Notes in Control and Information Sciences: Robot Motion and Control, K. R. Kozłowski (Ed.), pp. 185-195, 2009

  3. D. Belter, P. Skrzypczyński, Efficient Gait Learning In Simulation: Crossing the Reality Gap by Evolutionary Model Identification, 12th International Conference on Climbing and Walking Robots CLAWAR 2009, Istanbul, Turkey, September 9-11 2009

  4. D. Belter, P. Skrzypczyński, A Biologically Inspired Approach to Feasible Gait Learning for a Hexapod Robot, International Journal of Applied Mathematics and Computer Science, Vol. 20, No. 1, pp. 69-84, 2010 (pdf)