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

1. Simulation on the relative location recognition of Multi-Robots, using MRDS ROBOMEC 2010 in ASAHIKAWA Kyushu Institute of Technology Computer Science and System Engineering Kazuaki Tanaka Laboratory WONJAE CHO

2. Kyushu Institute of Technology Index 1 Introduction 2 Simulation System Configuration 3 Relative Location Recognition 4 Experiment Results 5 Conclusion

3. 1 1. Introduction 1.1 Objective It is important for a mobile robot to identify its current location for the autonomous execution of its given tasks in random space. Sorry.. Where am I ? Kyushu Institute of Technology

4. 2 1. Introduction 1.2 Cooperative Robot System However, Multi-robots requires a large initial production cost and a certain minimum size of experiment space for creating the system. Single Robot System Processing time faster than Multi-robots Cooperative Robot System Accomplish more complex task than Single robot can working alone. Reliable and Robust Complete the task much faster than Single robot (By working in Parallel) http://www.ornl.gov/info/ornlreview/v33_2_00/robots.htm Kyushu Institute of Technology

5. 3 1. Introduction 1.3 Simulation Application For Robot While there may be various solution to these issues, a simulation experiment in 3D-simulated space can be on of the answers. Microsoft Evolution OROCOS Skilligent URBI Webots No No No No Yes No Open source Free of Charge No Academic, Hobby No No No Yes Distributed Environment No Yes No Yes Yes No Real - time No No No No No No Range of Supported Hardware Large Small Medium Medium Large Large Simulation Environment Yes No No No Yes Yes Behavior Coordination Yes Yes No Yes Yes No Reusable Service Building block Yes Yes Yes Yes No Not App http://www.windowsfordevices.com/c/a/Windows-For-Devices-Articles/A-review-of-robotics-software-platforms/ Kyushu Institute of Technology

6. 4 1. Introduction 1.4 About MRDS MRDS 2008 R2 is a Windows-based environment for academic to easily create robotics applications across a wide variety of H/W. http://www.microsoft.com/robotics Kyushu Institute of Technology

7. 5 2. Simulation System Configuration 2.1 System Configuration Communication Service Orchestration Service Simulation Service Client Recognizes the location of the Client by using Sensor I/O Service of the Data (using UDP) 3D Modeling Runtime Environment Service Concurrency and Coordination Runtime (CCR) Decentralized Software Services(DSS) Fig. 1 System block diagram Kyushu Institute of Technology

8. 6 2. Simulation System Configuration 2.2 Simulation Robot Configuration Pioneer3DX(produced by Mobile Robots) Using SimpleDashBoard Service Motor Service (Control of 2 Wheels) Camera Service (Omni-Directional vision) Communication Service (UDP) Kyushu Institute of Technology

9. 7 2. System Configuration 2.3 Omni-directional Vision on Simulation The hardware-based method, which acquires panoramic images by using multiple cameras. Step 3: Display on Simulation Step 1 : Take a Picture Step 2 : Integrate Image Front Right Left Rear Sequence : Front -> Right -> Rear -> Left Appendix A Field of View = 90° Kyushu Institute of Technology

10. 8 2. System Configuration 2.3 Appendix A, Omni-Directional Image on the simulation Fig. 2 Panoramic Image on the Simulation Kyushu Institute of Technology

11. 9 3. Relative Location Recognition 3.0 Image Processing Configuration Preprocessing Calculating Of Position Client Template Matching Binaryadaptive method Normalized Gray-level Correlation(NGC) Image Data Using Relative Coordinate System 2. LabelingSequential Labeling Tracking of Relative Position Action Using Particle Filter Fig. 3 Block Diagram depicting the Relative Recognition Kyushu Institute of Technology

12. 10 3. Relative Location Recognition 3.1 Extracting the Region of Interest Step 2 : binary using adaptive method Step 3 : Sequential Labeling Step 1 : Histogram Analysis Kyushu Institute of Technology

13. 11 3. Relative Location Recognition 3.2 Template Matching (Normalized Gray-level Correlation: NGC) ‘i’ , ‘j’ : indices for the pixel a(i, j) : brightness of the compared section of the region – m b(i, j) : brightness of the template pattern – t Template size : M X N Kyushu Institute of Technology

14. 12 3. Relative Location Recognition 3.3 Calculating the Relative Locations of Robots y R3(x3, y3) d13 d23 Relative Coordinate d12 x R2(x2, y2) R1(x1, y1) World Coordinate Fig. 4 Relative Coordinate System among Robots Kyushu Institute of Technology

15. 13 4. Experiment Results 4.1 Experimental Environment The Robots were placed in 1m intervals and their locations were measured by simulated sensors. Virtual Environment1) Space : 10m X 10m X 1m2) Number of Robot : 3 units3) No Obstacles Experimental Performance1.86GHz and 1.5GB RAM 10m Kyushu Institute of Technology

16. 14 4. Experiment Results 4.2 Simulation Result UI The images collected for the location recognition, and the identified objects in each image are displayed. A. Drawing Relative Position of Robots on the map A B B. Extracting Regions of Interest on the simulation C. Resulting positions of the recognition process C Appendix B Kyushu Institute of Technology

17. 15 4. Experiment Results 4.2 Appendix B, Simulation video Kyushu Institute of Technology

18. 16 4. Experiment Results 4.3 Location Recognition Result and Error rates The average error rate is 5.26. Kyushu Institute of Technology

19. 17 5. Conclusion Simulation System has shown that relative locations of robots can be recognized by using simulated panoramic vision cameras. However, Since the current system recognizes only the locations of standstill robots, it cannot track the locations of moving robots. Therefore !! Kyushu Institute of Technology

20. 18 5. Conclusion A simulation system might be developed in the future that can track the locations of robots in motion, using particle filter. Fig. 4 tracking object using particle filter Kyushu Institute of Technology