An introduction to fuzzy control
An introduction to fuzzy control
Applications of fuzzy logic in the control of robotic manipulators
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Applying electric field sensing to human-computer interfaces
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Mobile Robotics: A Practical Introduction: History, Design, Analysis and Examples
Mobile Robotics: A Practical Introduction: History, Design, Analysis and Examples
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
Adaptive modelling, estimation and fusion from data: a neurofuzzy approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Robotic end effectors are used over a diverse range of applications where they are required to grip with optimal force to avoid the object be either dropped or crushed. The slipping state can be easily detected by the output of the slip sensor. When the output has a non-zero value, the object is slipping. Conversely, detecting the deformation (crushing) state is more difficult, especially in an unstructured environment. Current proposed methodologies are ad hoc and specialised to the particular object or objects to be handled. Consequently, the gripper can only manipulate prior known objects, constraining the gripper application to a small set of predetermined objects. Accordingly, in this paper, it is proposed a hybrid approach of fuzzy and expert systems that permits to detect when an unknown object is being deformed. To determinate when the gripped object is being deformed, the fuzzy/expert system uses information from three sensors: applied force, slip rate and finger position. Several objects of different characteristics were used to prove the effectiveness of the proposed approach.