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Evolutionary Design of a Control Architecture for Soccer-Playing Robots 221
the fitness function raises by factor of two. The algorithm finds a new route
within four time steps.
For this experiment, a (2+20)-strategy was used. Because the fitness func-
tion changes when the robot or the obstacles move, found solutions need to
be re-calculated in each step. Otherwise, the robot will not change its path as
a found solution remains valid.

6 Discussion

This chapter has given a short introduction to the world-wide RoboCup ini-
tiative. The focus was on the small-size league, where two teams of five robots
play soccer against each other. Since no human control is allowed, the system
has to control the robots in an autonomous way. To this end, a control soft-
ware analyzes images obtained by two cameras and then derives appropriate
control commands for all team members.
The omnidirectional drives used by most research teams exhibit certain
inaccuracies due to two physical effects called ‘slip’ and ‘friction’. Section 2 has
applied Kohonen feature maps to compensate for rotational and directional
drift caused by the two effects.
Unfortunately, the image processing system exhibits various time delays at
different stages, which leads to erroneous robot behavior. Sections 3 and 4 have
incorporated back-propagation networks in order to alleviate this problem by
learning techniques which enable precise predictions to be made.
The results presented in this chapter show that neural networks can sig-
nificantly improve the robot’s behavior with respect to accuracy, drift, and
response. Additional experiments, which are not discussed in this chapter,
have shown that these enhancements lead to an improved team behavior.
The experimental results have also revealed the following deficiencies: Both
Kohonen and back-propagation networks require a training phase prior to
the actual operation. This limits the networks’ online adaptation capabili-
ties. Furthermore, the architectures presented here still require hand-crafted
adjustments to some extent. In addition, the resources available on the mobile
robots significantly limit the complexity of the employed networks. Finally,
the usage of back-propagation networks create the two well-known problems
of over-learning and local minima.
Path planning based on evolutionary algorithms on a RoboCup small-size
league robot is a possible option. The implementation meets the real-time
constraints that are given by the robot’s hardware and the environment. The
algorithm is capable of finding a path from source to destination and to adapt
to environmental changes.
Future research will address the problems discussed above. For this goal,
the incorporation of short-cuts into the back-propagation networks seems to
be a promising option. The investigation of other learning and self-adaptive
principles, such as Hebbian learning [4], seems essential for developing truly