As robotic systems make their way into standard practice, they have
opened the door to a wide spectrum of complex applications. Such
applications usually demand that the robots be highly intelligent.
Future robots are likely to have greater sensory capabilities, more
intelligence, higher levels of manual dexter- ity, and adequate
mobility, compared to humans. In order to ensure high-quality control
and performance in robotics, new intelligent control techniques must be
developed, which are capable of coping with task complexity,
multi-objective decision making, large volumes of perception data and
substantial amounts of heuristic information. Hence, the pursuit of
intelligent autonomous robotic systems has been a topic of much
fascinating research in recent years. On the other hand, as emerging
technologies, Soft Computing paradigms consisting of complementary
elements of Fuzzy Logic, Neural Computing and Evolutionary Computation
are viewed as the most promising methods towards intelligent robotic
systems. Due to their strong learning and cognitive ability and good
tolerance of uncertainty and imprecision, Soft Computing techniques have
found wide application in the area of intelligent control of robotic
systems.