A biologically inspired approach to feasible gait learning for a hexapod robot

Dominik Belter; Piotr Skrzypczyński

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 1, page 69-84
  • ISSN: 1641-876X

Abstract

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The objective of this paper 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. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.

How to cite

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Dominik Belter, and Piotr Skrzypczyński. "A biologically inspired approach to feasible gait learning for a hexapod robot." International Journal of Applied Mathematics and Computer Science 20.1 (2010): 69-84. <http://eudml.org/doc/207978>.

@article{DominikBelter2010,
abstract = {The objective of this paper 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. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.},
author = {Dominik Belter, Piotr Skrzypczyński},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {evolutionary learning; legged robots; gait generation; model identification; reality gap},
language = {eng},
number = {1},
pages = {69-84},
title = {A biologically inspired approach to feasible gait learning for a hexapod robot},
url = {http://eudml.org/doc/207978},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Dominik Belter
AU - Piotr Skrzypczyński
TI - A biologically inspired approach to feasible gait learning for a hexapod robot
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 1
SP - 69
EP - 84
AB - The objective of this paper 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. This strategy enables the evolutionary algorithm to discover proper leg co-ordination rules for a hexapod robot, using only simple dependencies between the states of the legs and a simple fitness function. The dependencies used are inspired by typical insect behaviour, although we show that all the introduced rules emerge also naturally in the evolved gait patterns. Finally, the gaits evolved in simulations are shown to be effective in experiments on a real walking robot.
LA - eng
KW - evolutionary learning; legged robots; gait generation; model identification; reality gap
UR - http://eudml.org/doc/207978
ER -

References

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