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Next, the vision system segments the indicated object from the background, and plans a suitable grasp strategy.
Then, a control architecture for the whole system is proposed, with particular attention on a suitable grasp planning strategy.
Next, a grasp quality measure is presented to characterize a suitable grasp configuration and systematically control the failure behavior of the object.
They suggest that the grasp specification and evaluation modules run in an alternating manner until a suitable grasp is selected.
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In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook.
To tackle this question we further extended our previous study [15] by introducing a virtual individual such as an avatar in the visual scene and investigating whether the sight of objects located outside the reaching space of the participant but within the reaching space of the avatar might evoke the motor representation of a suitable grasping action as measured by the spatial alignment effect.
In addition, control algorithms have been tested for regulating the grasp force that is provided by each finger in order to achieve a suitable firm grasp even in dynamic conditions.
A 3D model of each gripper was constructed and suitable grasp quality measures were developed and validated in a physical environment.
Obtaining a good grasp requires algorithms to automatically determine proper contact points on the object as well as proper hand configurations, especially when dexterous manipulation is desired, and the quantification of a good grasp requires the definition of suitable grasp quality measures.
Specifically, suitable grasp configurations reducing gravitational and inertial effects during object manipulation, and minimizing and equally distributing the grasping forces among all the available fingers, must be computed.
The control system also includes a grasp-planning algorithm (developed in MATLAB) that determines the shortest path and suitable grasping point of micro-objects.
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