Integrating a flexible anthropomorphic, robot hand into the control, system of a humanoid robot

Osswald, Dirk; Martin, Jan; Burghart, Catherina; Mikut, Ralf; Wörn, Heinz; Bretthauer, Georg

doi:10.1016/j.robot.2004.07.005

Cited by 21 publications

(16 citation statements)

References 5 publications

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“…The question can be stated as: 'Is active control needed to perfectly mimic the human hand adaptation, or is as simple mechanical design with approximate adaptive grasp sufficient?' The question itself illustrates the difference in design strategies between active (computer controlled) adaptive grasp [5,6] and passive (mechanism control) adaptive grasp [7,8]. The gripper must implement three mechanical features that together give it the ability for passive adaptive grasp.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Petković

Shamshirband

Anuar

et al. 2015

J Intell Robot Syst

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The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

show abstract

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Petković

Shamshirband

Anuar

et al. 2015

J Intell Robot Syst

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show abstract

“…Some studies in robotics have proposed methods of interaction with environments using non-contact interaction such as using ultrasonic wave sensor, vision image processing and etc (Ogata et al, 2000, Cheng et al, 2001). However, some work reported the use of robotic armed mobile robot to analyze object surface by groping and obtain information to perform certain locomotion (Hashimoto et al, 1997, Kanda at al., 2002, Osswald et al, 2004. Overall there has been very little work reported about application of contact interaction on bipedal humanoid robots (Konno, 1999).…”

Section: Relevancy Of Contact Interaction In Humanoid Robot's Navigationmentioning

confidence: 99%

Humanoid Robot Navigation Based on Groping Locomotion Algorithm to Avoid an Obstacle

Yussof¹,

Yamano²,

Nasu³

et al. 2006

Mobile Robots: Towards New Applications

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“…Für die Erkennung von Greifmustern werden Methoden wie das Kontaktnetz (contact web) verwendet [88] In Anlehnung an die bisher behandelten analyseorientierten Greifklassifikationen wird eine neue, ausführungsorientierte Aufgabenklassifikation (Abbildung 2.6) für humanoide Roboter notwendig [146]. Die Einordnung erfolgt an den bereits eingeführten Aufgabentypen Gesten, Haltung und Greifen, wobei hier wie bei der Klassifikation in Abbildung 2.5 auch zwischen Kraft-und Präzisionsgriffen unterschieden wird.…”

Section: Programmieren Durch Vormachenunclassified

“…Die Einordnung erfolgt an den bereits eingeführten Aufgabentypen Gesten, Haltung und Greifen, wobei hier wie bei der Klassifikation in Abbildung 2.5 auch zwischen Kraft-und Präzisionsgriffen unterschieden wird. Abbildung 2.6: Grifftaxonomie für einen humanoiden Roboter, mit * markierte Griffmuster sind in unterschiedlichen Größen zu realisieren [146]. …”

Section: Programmieren Durch Vormachenunclassified

See 1 more Smart Citation

Ein Beitrag zur Integration von Sensoren in eine anthropomorphe künstliche Hand mit flexiblen Fluidaktoren

Martin¹

2005

At - Automatisierungstechnik

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Integrating a flexible anthropomorphic, robot hand into the control, system of a humanoid robot

Cited by 21 publications

References 5 publications

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

RETRACTED ARTICLE: Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

Humanoid Robot Navigation Based on Groping Locomotion Algorithm to Avoid an Obstacle

Ein Beitrag zur Integration von Sensoren in eine anthropomorphe künstliche Hand mit flexiblen Fluidaktoren

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