  Real time applications such as robotic require real time actions based on the
immediate available data. Machine learning and artificial intelligence rely on
high volume of training informative data set to propose a comprehensive and
useful model for later real time action. Our goal in this paper is to provide a
solution for robot grasping as a real time application without the time and
memory consuming pertaining phase. Grasping as one of the most important
ability of human being is defined as a suitable configuration which depends on
the perceived information from the object. For human being, the best results
obtain when one incorporates the vision data such as the extracted edges and
shape from the object into grasping task. Nevertheless, in robotics, vision
will not suite for every situation. Another possibility to grasping is using
the object shape information from its vicinity. Based on these Haptic
information, similar to human being, one can propose different approaches to
grasping which are called grasping policies. In this work, we are trying to
introduce a real time policy which aims at keeping contact with the object
during movement and alignment on it. First we state problem by system dynamic
equation incorporated by the object constraint surface into dynamic equation.
In next step, the suggested policy to accomplish the task in real time based on
the available sensor information will be presented. The effectiveness of
proposed approach will be evaluated by demonstration results.

Robotics