Soft Computing Applications in Robotic Vision Systems

1.1 Soft Computing Soft computing is a collection of intelligent techniques working in a complementary way to build robust systems at low cost. Soft computing includes techniques such as neural networks, fuzzy logic, evolutionary computation (including genetic algorithms) and probabilistic reasoning (Wang and Tang, 1997). These techniques are capable of dealing with imprecision, uncertainty, ambiguity, partial truth, machine learning and optimization issues we usually face in real world problems. Soft computing addresses problem solving tasks in a complementary approach more than in a competitive one. Main advantages of soft computing are: i) its rich knowledge representation (both at signal and pattern level), ii) its flexible knowledge acquisition process (including machine learning and learning from human experts) and iii) its flexible knowledge processing. These advantages let us to build intelligent systems with a high machine intelligence quotient at low cost. Soft computing systems have already been applied in industrial sectors like aerospace, communications systems, robotics and automation and transport systems (Dote and Ovaska, 2001).