Neural networks, fuzzy controls, and evolutionary computation have been used for behavior
learning of robots in unknown and/or dynamic environment [1-4]. However, it is very di cult to
design the learning structure of a robot beforehand, because the dynamics of the
environment is unknown. Consequently, the robot should have a high learning capability to
deal with the spatio-temporal context of the facing environment. Therefore, this paper
proposes a fuzzy spiking neural network (FSNN)[5] for behavior learning of a mobile robot …

It is very difficult to design the learning structure of a robot beforehand in an unknown and
dynamic environment, because the dynamics of the environment is unknown. Therefore, this
paper proposes a spiking neural network (SNN) for behavior learning of a mobile robot in a
dynamic environment including multiple moving obstacles. Furthermore, a behavior should
be acquired and improved according to previously acquired behaviors. This indicates the
robot should accumulate behavior knowledge through the lifetime learning. Therefore, we …