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The proposed optimization is shown to be globally convergent to the optimal grasping force.
Simulation results show that the proposed neural network can achieve optimal grasping force.
This paper addresses the problem of optimal grasping of an object with a multi-fingered robotic hand for accomplishing a given task.
Real applications of a circular cone lie in some engineering problems, for example, in the formulation for optimal grasping manipulation for multi-fingered robots, the grasping force of i th finger is subject to a circular cone constraint, see [10, 11] and references for more details.
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The capability to equilibrate external wrenches is crucial in optimal grasp planning.
Finally, an exhaustive search of the optimal grasp configurations with respect to the grasp quality is performed.
An optimal grasp is found as a grasp that is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context.
Note that optimal grasps are not necessarily optimal according to all criteria.
An optimal grasp under a force constraint is not necessarily optimal under another one.
Table 1 shows a numerical comparison of the quality values for the optimal grasps according to the above criteria.
The largest hypersphere inscribed in the FCS determines the optimal grasp, as shown in Fig. 10 b.
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CEO of Professional Science Editing for Scientists @ prosciediting.com