Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning techniques. The book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting implementation tactics, adequate application examples and illustrative interpretations.
Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect.
1. Chapter One. Introduction - p. 1 2. Chapter Two. Software systems and platforms for teleoperation - p. 17 3. Chapter Three. Uncertainties compensation-based teleoperation control - p. 39 4. Chapter Four. User experience-enhanced teleoperation control - p. 73 5. Chapter Five. Shared control for teleoperation - p. 113 6. Abstract - p. 113 7. 5.1 Introduction - p. page113 8. 5.2 Collision avoidance control - p. 114 9. 5.3 EEG-based shared control - p. page125 10. 5.4 MR-based user interactive path planning - p. page134 11. 5.5 No-target obstacle avoidance with sEMG-based control - p. 142 12. 5.6 APF-based hybrid shared control - p. 156 13. 5.7 Experimental case study - p. 163 14. 5.8 Conclusion - p. page182 15. References - p. 183 16. Chapter Six. Human–robot interaction in teleoperation systems - p. 187 17. Chapter Seven. Task learning of teleoperation robot systems - p. 211 18. Index - p. 245
Human-in-the-loop Learning and Control for Robot Teleoperation presents recent, research progress on teleoperation and robots, including human-robot interaction, learning and control for teleoperation with many extensions on intelligent learning techniques. The book integrates cutting-edge research on learning and control algorithms of robot teleoperation, neural motor learning control, wave variable enhancement, EMG-based teleoperation control, and other key aspects related to robot technology, presenting implementation tactics, adequate application examples and illustrative interpretations.
Robots have been used in various industrial processes to reduce labor costs and improve work efficiency. However, most robots are only designed to work on repetitive and fixed tasks, leaving a gap with the human desired manufacturing effect.
1. Chapter One. Introduction - p. 1 2. Chapter Two. Software systems and platforms for teleoperation - p. 17 3. Chapter Three. Uncertainties compensation-based teleoperation control - p. 39 4. Chapter Four. User experience-enhanced teleoperation control - p. 73 5. Chapter Five. Shared control for teleoperation - p. 113 6. Abstract - p. 113 7. 5.1 Introduction - p. page113 8. 5.2 Collision avoidance control - p. 114 9. 5.3 EEG-based shared control - p. page125 10. 5.4 MR-based user interactive path planning - p. page134 11. 5.5 No-target obstacle avoidance with sEMG-based control - p. 142 12. 5.6 APF-based hybrid shared control - p. 156 13. 5.7 Experimental case study - p. 163 14. 5.8 Conclusion - p. page182 15. References - p. 183 16. Chapter Six. Human–robot interaction in teleoperation systems - p. 187 17. Chapter Seven. Task learning of teleoperation robot systems - p. 211 18. Index - p. 245
