Object shape recognition and grasping by five-fingered robotic hand based on E-ANFIS model

Fan, Shaowei; Liu, Yiwei; Wu, Kebin; Chen, Zhaopeng; Jiang, Zainan; Jiang, Li; Hong, Li

doi:10.1109/robio.2009.5420858

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Telemanipulation research resulted in many current telemanipulation papers based on aspects of human manipulation including stable grasp, slip detection, and object recognition using anthropomorphic dexterous robotic hands [12], [25], [26], [27].…”

Section: Teleoperationmentioning

confidence: 99%

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

Welyhorsky

Fonseca

Zhu

et al. 2022

2022 IEEE International Systems Conference (SysCon)

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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.

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Section: Teleoperationmentioning

confidence: 99%

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

Welyhorsky

Fonseca

Zhu

et al. 2022

2022 IEEE International Systems Conference (SysCon)

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“…Taking advantage of the ANFIS controller for mobile robot navigation is presented in [40,41]. The application of multiple ANFIS models for object shape recognition is depicted in [42].…”

mentioning

confidence: 99%

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Sheikhsamad,

Suárez,

Rosell

2024

Machines

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Dexterous manipulation of unknown objects performed by robots equipped with mechanical hands represents a critical challenge. The difficulties arise from the absence of a precise model of the manipulated objects, unpredictable environments, and limited sensing capabilities of the mechanical hands compared to human hands. This paper introduces a data-driven approach that provides a learning-based planner for dexterous manipulation employing an Adaptive Neuro-Fuzzy Inference System (ANFIS) fed by data obtained from an analytical manipulation planner. ANFIS captures the complex relationships between inputs and optimal manipulation parameters. Moreover, during a training phase, it is able to fine-tune itself on the basis of its experiences. The proposed planner enables a robot to interact with objects of various shapes, sizes, and material properties while providing an adaptive solution for increasing robotic dexterity. The planner is validated in a real-world environment, applying an Allegro anthropomorphic robotic hand. A link to a video of the experiment is provided in the paper.

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Object shape recognition approach for sparse point clouds from tactile exploration

Jin

Fan

et al. 2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Object shape recognition and grasping by five-fingered robotic hand based on E-ANFIS model

Cited by 4 publications

References 21 publications

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

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

Learning-Based Planner for Unknown Object Dexterous Manipulation Using ANFIS

Object shape recognition approach for sparse point clouds from tactile exploration

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