Intelligent Control of Robotic Systems

1. Front Matter

   Pages i-xix

   PDF

2. Intelligent Control in Contemporary Robotics

   • Dusko Katic, Miomir Vukobratovic

   Pages 1-19

3. Neural Network Approach in Robotics

   • Dusko Katic, Miomir Vukobratovic

   Pages 21-70

4. Fuzzy Logic Approach in Robotics

   • Dusko Katic, Miomir Vukobratovic

   Pages 71-112

5. Genetic Algorithms in Robotics

   • Dusko Katic, Miomir Vukobratovic

   Pages 113-131

6. Hybrid Intelligent Approaches in Robotics

   • Dusko Katic, Miomir Vukobratovic

   Pages 133-154

7. Synthesis of Connectionist Control Algorithms for Robot Contact Tasks

   • Dusko Katic, Miomir Vukobratovic

   Pages 155-192

8. Synthesis of Comprehensive Connectionist Control Algorithms for Robot Contact Tasks

   • Dusko Katic, Miomir Vukobratovic

   Pages 193-221

9. Examples of Intelligent Techniques for Robotic Applications

   • Dusko Katic, Miomir Vukobratovic

   Pages 223-255

10. Back Matter

    Pages 257-296

    PDF

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.

• Fuzzy
• algorithms
• classification
• control algorithm
• development
• fuzzy logic
• genetic algorithms
• learning
• logic
• robot
• robotics

• Robotics Laboratory, Mihailo Pupin Institute Belgrade, Serbia, Serbia & Montenegro

  Dusko Katic, Miomir Vukobratovic

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