Fetch the ball, Oncilla!

Size: px

Start display at page:

Download "Fetch the ball, Oncilla!"

Amberlynn Angela Paul
6 years ago
Views:

1 Fetch the ball, Oncilla! Semester Project in Micro-engineering Laetitia Perroud Supervisor: Prof. Auke Jan Ijspeert Assistant: Mostafa Ajallooeian

2 Project description and motivation Goal : have the Oncilla robot follow a ball Ball tracking (where is the ball?) Locomotion toward the ball Part of the AMARSi project Nice behavior to make the robot look more like a real animal 2

3 First draft on a simplified world: Simplified world No locomotion Simple control Fixed robot with rotative camera which can also translate Goal : rotate the camera until the ball is centered and at a suitable distance 3

4 For each control step: Simplified world Take a picture with camera Threshold the image to keep only red (B) Calculate ball area and center of mass: Use PID-controllers on the rotation and translation to keep x0 in the middle and A around a certain value 4

5 Ball detection Works fine when image completely inside Detection when ball is partially inside the image Find 3 suitable points to estimate radius and center of the ball 5

6 Implementation on Oncilla Locomotion profiles Yaw offset Reference: M. Ajallooeian, S. Gay, A. Tuleu, A. Sprowitz and A. Ijspeert. Modular Control of Limit Cycle Locomotion over Unperceived Rough Terrain. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, Add vision and include it to the locomotion control 6

7 Implementation on Oncilla The robot should accelerate when the ball is far away and slow down when it comes closer PID-controller on the speed depending on the ball size The robot should turn in order to keep the ball in front on it PID-controller on the yaw_offset, which allows the robot to turn, to keep x0 in the middle of the image 7

8 Noise and Filter In order to reduce the noise, a weighted moving average filter is implemented on the ball area and center of mass. For each input data, the output is the weighted average of the N (50) previous data with decreasing weight. Before After 8

9 Ball area in number of pixels x0 normalized by image width Time [s] Time [s] Speed [m/s] Yaw offset = K*x0 10 Time [s] Time [s]

10 Implementation on the real robot Oncilla currently works on open loop Enough for flat ground On open loop there is no posture control Abduction / adduction movement not defined No turning available Reference: M. Ajallooeian, S. Gay, A. Tuleu, A. Sprowitz and A. Ijspeert. Modular Control of Limit Cycle Locomotion over Unperceived Rough Terrain. IEEE/RSJ International Conference on Intelligent 12 Robots and Systems (IROS), Tokyo, Japan, 2013.

11 Enable Turning Design a pattern for abduction/adduction Draw foot trajectory and use inverse kinematics Copy pattern from protraction/retraction joint Poor overall results in simulation Altering the foot trajectory Legs on one side will have shorter stride length than on the other side thus making the robot turn 13

12 Adding vision Simple webcam fixed in front of Oncilla 14

13 Image processing modification Goal : detect a ball of a given color in any environment Algorithm: Conversion from RGB to HSV Threshold on H and S to form binary image Reference: HSL and HSV. In Wikipedia. Retrieved , from HSV 15

14 Image processing modification Algorithm: Erosion filter to remove small blobs Project the image onto x and y axis Assuming the ball is the biggest blob, keep only the biggest 1D connected component on x and y Limit additional image processing to the box defined previously 16

15 17

16 Results Red 18

17 Results Blue 19

18 Results Green 20

19 Results Yellow 21

20 Implementation on on-board computer Vision library used : Video4Linux (V4L) It takes around 35ms to grab a picture and save it for further processing Too slow, cycle refresh time of Oncilla: 5ms! Solution: take picture and find ball location continuously in a parallel thread 22

21 Control and Tuning Control analog to simulation but with P-controllers Less agility because of open loop Maximum speed: 0.4m/s Turning coefficient bounded between -0.3 and 0.3 In place turning when no ball is seen Speed = 0.3m/s, turning coef = ±1 Moving average filter of length N=3 23

22 Noise reduction The ball detection algorithm will always detect the biggest object of the given color Problem when the ball is not in the field view 24

23 Noise reduction Solution: Discard any input values when the difference between the current value of the area and the previous one is more than a certain amount 25

24 Results 26

25 28

26 Conclusion Oncilla is following the ball if the ball is not moved too fast Possible improvements: Improve dynamics by: Faster computation time Closed loop control 29

Self-Reconfigurable Robots for Space Exploration Effect of compliance in modular robots structures on the locomotion Adi Vardi

Self-Reconfigurable Robots for Space Exploration Effect of compliance in modular robots structures on the locomotion Adi Vardi Prof. Auke Jan Ijspeert Stéphane Bonardi, Simon Hauser, Mehmet Mutlu, Massimo