Neuro-Fuzzy Grasp Control for a Teleoperated Five Finger Anthropomorphic Robotic Hand

Abstract

Robots should offer a human-like level of dexterity when handling objects if humans are to be replaced in dangerous and uncertain working environments. This level of dexterity for human-like manipulation must come from both the hardware, and the control.Exact replication of human-like degrees of freedom in mobility for anthropomorphic robotic hands are seen in bulky, costly, fully actuated solutions, while machine learning to apply some level of human-like dexterity in underacted solutions is unable to be applied to a various array of objects. This thesis presents experimental and theoretical contributions of a novel neurofuzzy control method for dextrous human grasping based on grasp synergies using a Human Computer Interface glove and upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand. Experimental results proved the efficiency of the proposed Adaptive Neuro-Fuzzy Inference Systems to grasp objects with high levels of accuracy. III AcknowledgementsI would like to say thank you to my supervisor, Dr. Emil M. Petriu, for his insight, advice, knowledge, and all the time he spent throughout the duration of my graduate studies for discussions and reviews.I would like to thank Sindhu Radhakrishnan, for the brainstorming sessions, as well as her family for their kindness and giving me their time to answer my questions.I would also like to thank all my friends that provided encouragement and entertainment throughout this process.Finally, a very important thank you to my family, and my parents, for their support.At every point I knew you were encouraging me. I always knew you were there.