Evolving complex robot behaviors
Information Sciences, 121 (1-2):
1--25 (1999)Abstract
Building robots is a tough job because the designer
has to predict the interactions between the robot and
the environment as well as to deal with them. One
solution to such difficulties in designing robots is to
adopt learning methods. The evolution-based approach is
a special method of machine learning and it has been
advocated to automate the design of robots. Yet, the
tasks achieved so far are fairly simple. In this work,
we first analyze the difficulties of applying
evolutionary approaches to synthesize robot controllers
for complicated tasks, and then suggest an approach to
resolve them. Instead of directly evolving a monolithic
control system, we propose to decompose the overall
task to fit in the behavior-based control architecture,
and then to evolve the separate behavior modules and
arbitrators using an evolutionary approach.
Consequently, the job of defining fitness functions
becomes more straightforward and the tasks easier to
achieve. To assess the performance of the developed
approach, we evolve a control system to achieve an
application task of box-pushing as an example.
Experimental results show the promise and efficiency of
the presented approach.
