Vision Controlled Grasping by Means of an Intelligent Robot Hand

Milighetti, Giulio; Ritter, Moritz; Kuntze, H.-B.

doi:10.1007/978-3-642-01213-6_20

Cited by 2 publications

(2 citation statements)

References 9 publications

(11 reference statements)

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“…Since the CVS implements a robust estimation rather than a fine and sensitive analysis, the new components will add a marginal load to the standard procedures for the hand maintenance (i.e., the lenses will have to be kept relatively clean). For one example of a camera that is actually a part of the robotic hand, see [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation

Došen

Cipriani

Kostić

et al. 2010

J NeuroEngineering Rehabil

124

106

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BackgroundDexterous prosthetic hands that were developed recently, such as SmartHand and i-LIMB, are highly sophisticated; they have individually controllable fingers and the thumb that is able to abduct/adduct. This flexibility allows implementation of many different grasping strategies, but also requires new control algorithms that can exploit the many degrees of freedom available. The current study presents and tests the operation of a new control method for dexterous prosthetic hands.MethodsThe central component of the proposed method is an autonomous controller comprising a vision system with rule-based reasoning mounted on a dexterous hand (CyberHand). The controller, termed cognitive vision system (CVS), mimics biological control and generates commands for prehension. The CVS was integrated into a hierarchical control structure: 1) the user triggers the system and controls the orientation of the hand; 2) a high-level controller automatically selects the grasp type and size; and 3) an embedded hand controller implements the selected grasp using closed-loop position/force control. The operation of the control system was tested in 13 healthy subjects who used Cyberhand, attached to the forearm, to grasp and transport 18 objects placed at two different distances.ResultsThe system correctly estimated grasp type and size (nine commands in total) in about 84% of the trials. In an additional 6% of the trials, the grasp type and/or size were different from the optimal ones, but they were still good enough for the grasp to be successful. If the control task was simplified by decreasing the number of possible commands, the classification accuracy increased (e.g., 93% for guessing the grasp type only).ConclusionsThe original outcome of this research is a novel controller empowered by vision and reasoning and capable of high-level analysis (i.e., determining object properties) and autonomous decision making (i.e., selecting the grasp type and size). The automatic control eases the burden from the user and, as a result, the user can concentrate on what he/she does, not on how he/she should do it. The tests showed that the performance of the controller was satisfactory and that the users were able to operate the system with minimal prior training.

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Section: Discussionmentioning

confidence: 99%

Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation

Došen

Cipriani

Kostić

et al. 2010

J NeuroEngineering Rehabil

124

106

View full text Add to dashboard Cite

show abstract

“…The detection and the position estimation of objects without touching them is impossible with these sensors, so strategies to find objects in the robot's workspace have been developed. One approach to detect objects in the workspace of a robot gripper a camera has been built into a robot hand [5]. The hand camera serves as an additional sensor input to the robot's stereo vision system.…”

Section: Introductionmentioning

confidence: 99%

A tactile proximity sensor

2010

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This paper introduces a novel tactile sensor with the ability to detect objects in the sensor's near proximity. For both tasks, the same capacitive sensing principle is used. The tactile part of the sensor provides a tactile sensor array enabling the sensor to gather pressure profiles of the mechanical contact area. Several tactile sensors have been developed in the past. These sensors lack the capability of detecting objects in their near proximity before a mechanical contact occurs. Therefore, we developed a tactile proximity sensor, which is able to measure the current flowing out of or even into the sensor. Measuring these currents and the exciting voltage makes a calculation of the capacitance coupled to the sensor's surface and, using more sensors of this type, the change of capacitance between the sensors possible. The sensor's mechanical design, the analog/digital signal processing and the hardware efficient demodulator structure, implemented on a FPGA, will be discussed in detail.

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Vision Controlled Grasping by Means of an Intelligent Robot Hand

Cited by 2 publications

References 9 publications

Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation

Cognitive vision system for control of dexterous prosthetic hands: Experimental evaluation

A tactile proximity sensor

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