International Journal of Industrial Robot, Special Issue on Robot Control and Programming,

1. Tracking a moving object with real-time obstacle avoidanceChung-Hao Chen, Chang Cheng, David Page, Andreas Koschan and Mongi AbidiImaging, Robotics and Intelligent Systems Lab, Department of Electrical and Computer Engineering, The University of Tennessee, Knoxville, Tennessee, USA International Journal of Industrial Robot, Special Issue on Robot Control and Programming, Vol. 33, No. 6, pp. 460-468, 2006. Presented by：曹憲中

2. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

3. Introduction • The contributions of this paper are to present a mobile robotic system which can simultaneously track a moving object and avoid obstacles in real-time.

4. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

5. System architecture

6. System architecture

7. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

8. Image Input Phase • The Logitech Web Camera has a fixed view and is attached to the robotic platform. It is used to acquire color 320x240 images.

9. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

10. Object Tracking Phase • Lucas and Kanade's algorithm M represents the mass motion vector of the tracked object (in 1x2 matrix form). Xi represents each motion vector of the tracked object (in 1x2 matrixform). N represents amount of total motion vector.

11. Object Tracking Phase Conversion from image to 2D world coordinate system.

12. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

13. Robot Control Phase Conversion from image to 2D world coordinate system.

14. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

15. Obstacle Detection Phase • It uses a range scanner to sense if there is any obstacle in its projected path. • If no obstacle is detected, the robot mobility phase is activated. Subsequently, the control of the system returns back to the image input phase. • Otherwise, the system uses the obstacle avoidance phase for generating another robot control command in order to avoid the obstacle.

16. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

17. Obstacle Avoidance Phase • They propose a new algorithm called dynamic goal potential fields (DGPF) which is based on the traditional Potential Fields methods to solve this type of problems.

18. The DGPF algorithm is based on the following: • Using the current configuration, goal configuration and sensor data, it runs a basic potential fields algorithm to predict a path; • If the goal configuration does not change too much, then the robot follows this path to avoid any obstacle; • If the goal configuration moves to a new position which has a big change from the old position, the algorithm randomly chooses some points in the predicted path and runs the basic Potential Fields method to compute several paths starting from these points based on current sensor data; • The path with the lowest cost is selected (based on Euclidian distance). The robot is now using the new path to move to the new goal configuration.

19. Obstacle Avoidance Phase

20. Obstacle Avoidance Phase The speed of the object is 2 m/s, and the robot step size is 12 cm.

21. Obstacle Avoidance Phase The speed of the object is 2 m/s, and the robot step size is 50 cm.

22. Obstacle Avoidance Phase • A better solution is to use a dynamic step size. • When the object is moving slowly, a large step size is chosen to let the robot avoid the obstacle quickly. • Conversely, a relatively small step size is set to allow the robot to choose a better adjusted path to move towards a new position when the object is moving quickly.

23. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

24. Experimental Results

25. Experimental Results

26. Experimental Results

27. Experimental Results

28. Experimental Results

29. Experimental Results

30. Experimental Results

31. Outline • Introduction • System architecture • Image Input Phase • Object Tracking Phase • Robot Control Phase • Obstacle Detection Phase • Obstacle Avoidance Phase • Experimental Results • Conclusion

32. Conclusion • The system uses two sensors: a visual camera to sense the movement of any tracked object, and a range sensor to help the robot detect and then avoid obstacles in real-time while continuing to track the object. • This paper also presents a modified Potential Fields method called DGPF method which is used to deal with real-time obstacle avoidance for object tracking.

33. Thank you for your attention.