English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Real-time policy generation and its application to robot grasping