<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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 <italic>tahula rasa</italic> 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.</p></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.

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

Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965, Poznań, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"A biologically inspired approach to feasible gait learning for a hexapod robot"}

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...

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

Published Online: Mar 25, 2010

Page range: 69 - 84

DOI: https://doi.org/10.2478/v10006-010-0005-7

Keywords
evolutionary learning, legged robots, gait generation, model identification, reality gap

This content is open access.

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

Published Online: Mar 25, 2010

Page range: 69 - 84

DOI: https://doi.org/10.2478/v10006-010-0005-7

Keywordsevolutionary learning, legged robots, gait generation, model identification, reality gap

This content is open access.

Keywords
evolutionary learning, legged robots, gait generation, model identification, reality gap