This document summarizes a presentation on a novel approach for robot grasping based on cloud computing. The presentation was given by Mr. Krishna Kangane, Miss. Nikita Jadhav, Mr. Abhijeet Tote, and Mr. Atul Sathe, guided by Ms. Neelima Ambekar of Matoshri College of Engineering and Research Centre in Nasik. It discusses using cloud technologies like cloud computing, storage and the internet to create a converged infrastructure for robots. This allows robots to benefit from powerful computational resources in data centers and share information. The presentation covers the scope of the project, literature review on previous related work, the proposed system architecture including online and offline phases, a flowchart,

1. A Novel Approach for Robot
Grasping based on Cloud
Presented by: Mr Krishna Kangane
Miss. Nikita Jadhav
Mr. Abhijeet Tote
Mr. Atul Sathe
Guide: Ms. Neelima Ambekar
Matoshri College of Engineering and Research Centre, Nasik.

2. Contents
Introduction
Scope of Project
Literature Survey
System Architecture
Flowchart
System Requirements
Advantages and Disadvantages
Applications
Conclusion and Future Scope
References

3. Introduction
Cloud
Cloud Robot and Automation
systems
Cloud robotics based on major
points

4. Scope of project
• Cloud robotics using cloud computing,
cloud storage, and other Internet
technologies etc creates converged
infrastructure and shared services.
• It allows robots to benefit from the
powerful computational, storage, and
communications resources of modern
data centres.

5. Literature Survey
Sr.
No.
Year Author Name Paper Title Work Done
1. 1994 K. Goldberg Beyond the web: Excavating the real
world viamosaic
The first Industrial
robot was connected to
the Web with an
intuitive graphical user
interface that allowed
visitors
to tele operate the robot
via any nternet browser
2. 1997 M.Inaba et.al Remote-brained robots Described
the advantages of remote
computing for robot
control

6. Literature Survey (Contd.)
3. 2009 M. Waibel Robert Robert project was
announced
4. 2010 M. Tenorth Representing and exchanging
knowledge about
actions, objects, environments
Cloud Robotics
5. 2012 P. C. Evans and
M. Annunziata,
Pushing the boundaries
of minds and machines General
Electric
General Electric
introduced the term
“Industrial Internet
6 2013 V.
Chandrasekaran
and M. I. Jordan
Computational and statistical tradeoffs
via convex relaxation
Data sharing
statistics
7 2014 W. Beksi and N.
Papanikolopoulos
Point cloud culling for robot vision
tasks under communication constraints
Robot vision for
image grasping.

7. Architecture
Online Phase:

8. Architecture (Contd.)
Offline Phase

9. Flowchart

10. Requirements
Willow Garage PR2 robot with on-
board colour and depth cameras.
Google goggles image recognition
system
Point Cloud Library (PCL)
Open Robotics Automation Virtual
Environment (OpenRave).

11. Advantages
Make the robot smarter.
Longer battery life.
Invisible software and
hardware upgrade
Access to vast amount of
data
Removes overheads for
maintenance and updates
Reduces dependence on
custom middleware.

12. Disadvantages
Data privacy and security
Environmental security
Ethic Problems

13. Applications
SLAM
Grasping
Navigation

14. Conclusion
It allows the deployment of inexpensive
robots with low computation power and
memory requirements by working on the
communications network and the elastic
computing resources offered by the
cloud infrastructure
Cloud robotics allows robots to share
computation resources, information and
data with each other, and to access new
knowledge and skills not learned by
themselves.

15. Future Scope
The connectivity inherent in the Cloud raises a range of
privacy and security concerns.
On the technical front, new algorithms and methods are
needed to cope with time-varying network latency and
Quality-of-Service.
New algorithms are also needed that scale to the size of Big
Data, which often contain dirty data that requires new
approaches to clean or sample effectively

16. References
• Stephan Gammeter, Alexander Gassmann, Lukas Bossard,
Till Quack, and Luc Van Gool, ”Server- side Object
Recognition and Client-side Object Tracking for Mobile
Augmented Reality.”, In IEEE Computer Society
Conference on Computer Vision and Pattern Recognition,
number C, pages 1–8. IEEE, June 2010.
• Rajesh Arumugam, V.R. Enti, Liu Bingbing, Wu Xiaojun,
Krish- namoorthy Baskaran, F.F. Kong, A.S. Kumar, K.D.
Meng, and G.W. Kit. DavinCi, ”A Cloud Computing
Framework for Service Robots”, In IEEE International
Conference on Robotics and Automation, pages 3084–
3089. IEEE, 2010.
• Y. Hirano, K. Kitahama, and S. Yoshizawa. “Image-based
Object Recognition and Dexterous Hand/Arm Motion
Planning Using RRTs for Grasping in Cluttered Scene”. In
IEEE/RSJ International Conference on Intelligent Robots
and Systems, pages 2041–2046. IEEE, 2005.

17. References(contd.)
• Ben Kehoe, Dmitry Berenson, and Ken Goldberg, “Toward
Cloud-based Grasping with Uncertainty in Shape:
Estimating Lower Bounds on Achieving Force Closure with
Zero-slip Push Grasps” In IEEE International Conference on
Robotics and Automation, pages 576– 583. IEEE, May 2012.

18. Any Questions???