In-Hand Telemanipulation Using a Robotic Hand and Biology-Inspired Haptic Sensing

Fonseca, Vinícius Prado da; Lima, Bruno Monteiro Rocha; Oliveira, Thiago Eustaquio Alves de; Zhu, Qi; Groza, Voicu; Petriu, Emil M.

doi:10.1109/memea.2019.8802139

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…In industry, HIS is usually used to enhance the operation ability of robots. For example, the work in [8] equiped robot with bionic haptic manipulators to help it having more stable grasping ability in teleoperation tasks. In [14], the operator controls the robot to perform teleoperation in real time by means of a pneumatic haptic feedback glove.…”

Section: Related Workmentioning

confidence: 99%

Timestamp-independent Haptic-Visual Synchronization

Xu¹,

Huang²,

Zhao³

et al. 2022

Preprint

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The booming haptic data significantly improves the users' immersion during multimedia interaction. As a result, the study of Haptic, Audio-Visual Environment (HAVE) has attracted attentions of multimedia community. To realize such a system, a challenging task is the synchronization of multiple sensorial signals that is critical to user experience. Despite of audio-visual synchronization efforts, there is still a lack of haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic-visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a hapticvisual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics, and a key frame extraction of visual signals based on deep object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic-visual signals. Without timestamps in signal flow, the proposed method is still effective and more robust to complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.

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Section: Related Workmentioning

confidence: 99%

Timestamp-independent Haptic-Visual Synchronization

Xu¹,

Huang²,

Zhao³

et al. 2022

Preprint

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show abstract

“…In anatomy, the human hand is defined as "the portion of the upper limb distal to the radiocarpal joint, comprising the wrist, palm, and fingers" [11]. The human hand has 27 bones, 14 phalanges for the fingers and thumbs and 13 metacarpal and carpal bones defined in [12]. More commonly heard when discussing an infantry formation from Alexandrian tactics, the Greek word "phalange", or "phalanx", literally means "log" and describes the bones in our fingers.…”

Section: The Human Handmentioning

confidence: 99%

“…However, in primates, the hand is unique. The hands of apes, monkeys, and humans have opposable thumbs [12]. This allows for advanced dexterity, grasping, and intricate hand movements that could never be possible to other creatures.…”

Section: The Human Handmentioning

confidence: 99%

See 1 more Smart Citation

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

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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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“…In industry, an HIis usually used to enhance the operational ability of robots. For example, the work in [ 13 ] equipped a robot with bionic haptic manipulators to help it have more stable grasping ability in tele-operation tasks. In [ 14 ], the operator controls the robot to perform the tele-operation in real time by means of a pneumatic haptic feedback glove.…”

Section: Introductionmentioning

confidence: 99%

A Timestamp-Independent Haptic–Visual Synchronization Method for Haptic-Based Interaction System

Huang

Zhao

et al. 2022

Sensors

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The booming haptic data significantly improve the users’ immersion during multimedia interaction. As a result, the study of a Haptic-based Interaction System has attracted the attention of the multimedia community. To construct such a system, a challenging task is the synchronization of multiple sensorial signals that is critical to the user experience. Despite audio-visual synchronization efforts, there is still a lack of a haptic-aware multimedia synchronization model. In this work, we propose a timestamp-independent synchronization for haptic–visual signal transmission. First, we exploit the sequential correlations during delivery and playback of a haptic–visual communication system. Second, we develop a key sample extraction of haptic signals based on the force feedback characteristics and a key frame extraction of visual signals based on deep-object detection. Third, we combine the key samples and frames to synchronize the corresponding haptic–visual signals. Without timestamps in the signal flow, the proposed method is still effective and more robust in complicated network conditions. Subjective evaluation also shows a significant improvement of user experience with the proposed method.

show abstract

In-Hand Telemanipulation Using a Robotic Hand and Biology-Inspired Haptic Sensing

Cited by 8 publications

References 23 publications

Timestamp-independent Haptic-Visual Synchronization

Timestamp-independent Haptic-Visual Synchronization

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

A Timestamp-Independent Haptic–Visual Synchronization Method for Haptic-Based Interaction System

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