Your SlideShare is downloading. ×
0
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
100519 ver final
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

100519 ver final

1,098

Published on

This paper presents a simulation on the relative location recognition of multi-robots using MRDS (Microsoft Robotics Developer Studio). Multiple robots allow a more robust recognition of their …

This paper presents a simulation on the relative location recognition of multi-robots using MRDS (Microsoft Robotics Developer Studio). Multiple robots allow a more robust recognition of their relative locations in accuracy and performance compared to having just a single robot client. However, in order to experiment with a cooperative robot system that utilizes multiple robots units, there are constraints relating to large initial production costs for the robot units as well as having to secure a certain size of space for the experiment. This paper has resolved those issues by performing the multi-robot relative location recognition experiment in a simulated virtual space using MRDS, a robot application tool. Moreover, relative coordinates of robots were recognized by using omni-directional panoramic vision sensors on the simulation program

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
1,098
On Slideshare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
16
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 1. Simulation on the relative location recognition of Multi-Robots, using MRDS<br />ROBOMEC 2010 in ASAHIKAWA<br />Kyushu Institute of Technology<br />Computer Science and System Engineering<br />Kazuaki Tanaka Laboratory<br />WONJAE CHO<br />
  • 2. Kyushu Institute of Technology<br />Index<br />1<br />Introduction<br />2<br />Simulation System Configuration<br />3<br />Relative Location Recognition<br />4<br />Experiment Results<br />5<br />Conclusion<br />
  • 3. 1<br />1. Introduction<br />1.1 Objective<br />It is important for a mobile robot to identify its current location for the autonomous execution of its given tasks in random space.<br />Sorry..<br />Where am I ?<br />Kyushu Institute of Technology<br />
  • 4. 2<br />1. Introduction<br />1.2 Cooperative Robot System<br />However, Multi-robots requires a large initial production cost and a certain minimum size of experiment space for creating the system.<br />Single Robot System<br />Processing time faster than Multi-robots<br />Cooperative Robot System<br />Accomplish more complex task than Single robot can working alone.<br />Reliable and Robust<br />Complete the task much faster than Single robot (By working in Parallel)<br />http://www.ornl.gov/info/ornlreview/v33_2_00/robots.htm<br />Kyushu Institute of Technology<br />
  • 5. 3<br />1. Introduction<br />1.3 Simulation Application For Robot<br />While there may be various solution to these issues, a simulation experiment in 3D-simulated space can be on of the answers.<br />Microsoft<br />Evolution<br />OROCOS<br />Skilligent<br />URBI<br />Webots<br />No<br />No<br />No<br />No<br />Yes<br />No<br />Open source<br />Free of Charge<br />No<br />Academic,<br />Hobby<br />No<br />No<br />No<br />Yes<br />Distributed<br />Environment<br />No<br />Yes<br />No<br />Yes<br />Yes<br />No<br />Real - time<br />No<br />No<br />No<br />No<br />No<br />No<br />Range of<br />Supported<br />Hardware<br />Large<br />Small<br />Medium<br />Medium<br />Large<br />Large<br />Simulation<br />Environment<br />Yes<br />No<br />No<br />No<br />Yes<br />Yes<br />Behavior<br />Coordination<br />Yes<br />Yes<br />No<br />Yes<br />Yes<br />No<br />Reusable<br />Service<br />Building block<br />Yes<br />Yes<br />Yes<br />Yes<br />No<br />Not App<br />http://www.windowsfordevices.com/c/a/Windows-For-Devices-Articles/A-review-of-robotics-software-platforms/<br />Kyushu Institute of Technology<br />
  • 6. 4<br />1. Introduction<br />1.4 About MRDS<br />MRDS 2008 R2 is a Windows-based environment for academic to easily create robotics applications across a wide variety of H/W.<br />http://www.microsoft.com/robotics<br />Kyushu Institute of Technology<br />
  • 7. 5<br />2. Simulation System Configuration<br />2.1 System Configuration<br />Communication<br />Service<br />Orchestration<br />Service<br />Simulation<br />Service<br />Client<br />Recognizes the location of the Client by using Sensor<br />I/O Service of the Data<br />(using UDP) <br />3D Modeling<br />Runtime Environment Service<br />Concurrency and Coordination Runtime (CCR)<br />Decentralized Software<br />Services(DSS)<br />Fig. 1 System block diagram<br />Kyushu Institute of Technology<br />
  • 8. 6<br />2. Simulation System Configuration<br />2.2 Simulation Robot Configuration<br /> Pioneer3DX(produced by Mobile Robots)<br /> Using SimpleDashBoard Service<br /> Motor Service (Control of 2 Wheels)<br /> Camera Service (Omni-Directional vision)<br /> Communication Service (UDP)<br />Kyushu Institute of Technology<br />
  • 9. 7<br />2. System Configuration<br />2.3 Omni-directional Vision on Simulation<br />The hardware-based method, which acquires panoramic images by using multiple cameras.<br />Step 3: Display on Simulation<br />Step 1 : Take a Picture <br />Step 2 : Integrate Image<br />Front<br />Right<br />Left<br />Rear<br />Sequence : <br />Front -&gt; Right -&gt; Rear -&gt; Left<br />Appendix A<br />Field of View = 90°<br />Kyushu Institute of Technology<br />
  • 10. 8<br />2. System Configuration<br />2.3 Appendix A, Omni-Directional Image on the simulation<br />Fig. 2 Panoramic Image on the Simulation<br />Kyushu Institute of Technology<br />
  • 11. 9<br />3. Relative Location Recognition<br />3.0 Image Processing Configuration<br />Preprocessing<br />Calculating <br />Of Position<br />Client<br />Template Matching<br />Binaryadaptive method<br />Normalized Gray-level Correlation(NGC) <br />Image Data<br />Using Relative Coordinate System<br />2. LabelingSequential Labeling<br />Tracking of Relative Position <br />Action<br />Using Particle Filter<br />Fig. 3 Block Diagram depicting the Relative Recognition<br />Kyushu Institute of Technology<br />
  • 12. 10<br />3. Relative Location Recognition<br />3.1 Extracting the Region of Interest<br />Step 2 : binary using adaptive method<br />Step 3 : Sequential Labeling<br />Step 1 : Histogram Analysis<br />Kyushu Institute of Technology<br />
  • 13. 11<br />3. Relative Location Recognition<br />3.2 Template Matching (Normalized Gray-level Correlation: NGC)<br />‘i’ , ‘j’ : indices for the pixel<br />a(i, j) : brightness of the compared section of the region – m<br />b(i, j) : brightness of the template pattern – t<br />Template size : M X N<br />Kyushu Institute of Technology<br />
  • 14. 12<br />3. Relative Location Recognition<br />3.3 Calculating the Relative Locations of Robots<br />y<br />R3(x3, y3)<br />d13<br />d23<br />Relative Coordinate<br />d12<br />x<br />R2(x2, y2)<br />R1(x1, y1)<br />World Coordinate<br />Fig. 4 Relative Coordinate System among Robots<br />Kyushu Institute of Technology<br />
  • 15. 13<br />4. Experiment Results<br />4.1 Experimental Environment<br />The Robots were placed in 1m intervals and their locations were measured by simulated sensors.<br />Virtual Environment1) Space : 10m X 10m X 1m2) Number of Robot : 3 units3) No Obstacles<br />Experimental Performance1.86GHz and 1.5GB RAM<br />10m<br />Kyushu Institute of Technology<br />
  • 16. 14<br />4. Experiment Results<br />4.2 Simulation Result UI<br />The images collected for the location recognition, and the identified objects in each image are displayed.<br />A. Drawing Relative Position of Robots on the map<br />A<br />B<br />B. Extracting Regions of Interest on the simulation<br />C. Resulting positions of the recognition process<br />C<br />Appendix B<br />Kyushu Institute of Technology<br />
  • 17. 15<br />4. Experiment Results<br />4.2 Appendix B, Simulation video<br />Kyushu Institute of Technology<br />
  • 18. 16<br />4. Experiment Results<br />4.3 Location Recognition Result and Error rates<br />The average error rate is 5.26.<br />Kyushu Institute of Technology<br />
  • 19. 17<br />5. Conclusion<br />Simulation System has shown that relative locations of robots can be recognized by using simulated panoramic vision cameras.<br />However,<br />Since the current system recognizes only the locations of standstill robots, it cannot track the locations of moving robots.<br />Therefore !!<br />Kyushu Institute of Technology<br />
  • 20. 18<br />5. Conclusion<br />A simulation system might be developed in the future that can track the locations of robots in motion, using particle filter.<br />Fig. 4 tracking object using particle filter<br />Kyushu Institute of Technology<br />

×