Teleoperated Grasping Using a Robotic Hand and a Haptic-Feedback Data Glove

Zhu, Qi; Fonseca, Vinícius Prado da; Lima, Bruno Monteiro Rocha; Welyhorsky, Maxwell; Goubran, Miriam; Oliveira, Thiago Eustaquio Alves de; Petriu, Emil M.

doi:10.1109/syscon47679.2020.9275927

Cited by 9 publications

(7 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The same inputs were used as in [10] while using the individual finger synergies described in 4.2.2. In order to select the model which best simulates accurate servo motor action, training and testing of the FIS was required for all of the possible membership functions provided by the ANFIS toolbox.…”

Section: Discussionmentioning

confidence: 99%

“…Qi Zhu et al recently published [10] results of their research on teleoperated grasping using a robotic hand and haptic feedback data glove, which uses a modified 3D printed five fingered robotic hand to perform stable grasping in a similar way to Liarokapis et al [28] who performed similar feats of teleoperation and grasping using a five fingered robotic hand. One main difference is that the effort of Liarokapis is mapping the kinematics of the human hand.…”

Section: Teleoperationmentioning

confidence: 99%

“…Teleoperated Grasping using the Ring Ada Robotic Hand. This chapter further details the work of the research team reported in[10]. It includes the experimental results of the kinematic and postural synergy designs, as well as the feedback strategy used during the robotic hand grasping.Chapter 5.…”

mentioning

confidence: 99%

“…Figure 3.5 Back view of the Ring Ada hand (left), the side view of the Ring Ada with 'Claw'behavior (middle), and the palm view of the Ring Ada hand with FSR pressure sensors on each fingertip and on the palm (right)[10] …”

mentioning

confidence: 99%

See 3 more Smart Citations

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

Welyhorsky

Fonseca

Zhu

et al. 2022

2022 IEEE International Systems Conference (SysCon)

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Teleoperationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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

Welyhorsky

Fonseca

Zhu

et al. 2022

2022 IEEE International Systems Conference (SysCon)

Self Cite

View full text Add to dashboard Cite

show abstract

“…By wearing a data glove, operators can manipulate mechanical arms in real time by changing hand movements. This offers higher operation precision and freedom of freedom, particularly during tasks such as grasping or placement, demonstrating the immense potential of data gloves [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Teleoperated Grasping Using Data Gloves Based on Fuzzy Logic Controller

Lu,

Jin,

Liu

et al. 2024

Biomimetics

View full text Add to dashboard Cite

Teleoperated robots have attracted significant interest in recent years, and data gloves are one of the commonly used devices for their operation. However, existing solutions still encounter two challenges: the ways in which data gloves capture human operational intentions and achieve accurate mapping. In order to address these challenges, we propose a novel teleoperation method using data gloves based on fuzzy logic controller. Firstly, the data are collected and normalized from the flex sensors on data gloves to identify human manipulation intentions. Then, a fuzzy logic controller is designed to convert finger flexion information into motion control commands for robot arms. Finally, experiments are conducted to demonstrate the effectiveness and precision of the proposed method.

show abstract