Embedded Robotics (Thomas Braunl, 2 ed, 2006)

17 Real-Time Image Processing

0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 1 1 1

0 0 0 0 0 0 0 0 0 0 1 1 0

0 0 0 0 0 0 0 0 0 0 0 1 0

0 2 0 0 0 0 0 0 0 0 0 0 0

2 2 2 0 0 0 0 0 0 0 0 2 2

2 2 2 2 2 0 2 0 0 2 2 2 2

2 2 2 2 2 2 2 2 2 2 2 2 2

Figure 17.10: Segmentation example

If, as for many applications, identifying rectangular areas is sufficient, then the task becomes relatively simple. For now, we assume there is at most a single coherent object of each color class present in the image. For more objects of the same color class, the algorithm has to be extended to check for coherence. In the simple case, we only need to identify four parameters for each color class, namely top left and bottom right corner, or in coordinates:

[xtl, ytl], [xbr, ybr]

Finding these coordinates for each color class still requires a loop over all pixels of the segmented image, comparing the indices of the current pixel position with the determined extreme (top/left, bottom/right) positions of the previously visited pixels of the same color class.

17.8 Image Coordinates versus World Coordinates

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Image Coordinates versus World Coordinates

view and a top view from the robot’s local coordinate system. What we have to determine is the relationship between the ball position in local coordinates [x´, y´] and the ball position in image coordinates [j, i]:

y´ = f (i, h, D, f, d) x´ = g (j, 0, E, f, d)

Figure 17.12: Camera position and orientation

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It is obvious that f and g are the same function, taking as parameters:

•One-dimensional distance in image coordinates (object’s length in image rows or columns in pixels)

•Camera offset

(height in y´z´ view, 0 side offset in x´y´ view)

•Camera rotation angle (tilt or pan)

•Camera focal length

(distance between lens and sensor array)

•Ball size (diameter d)

Provided that we know the detected object’s true physical size (for example golf ball for robot soccer), we can use the intercept theorem to calculate its local displacement. With a zero camera offset and a camera angle of zero (no tilting or panning), we have the proportionality relationships:

These can be simplified when introducing a camera-specific parameter g = k · f for converting between pixels and meters:

y´ = g · d / i x´ = g · d / j

So in other words, the larger the image size in pixels, the closer the object is. The transformation is just a constant linear factor; however, due to lens distortions and other sources of noise these ideal conditions will not be observed in an experiment. It is therefore better to provide a lookup table for doing the transformation, based on a series of distance measurements.

With the camera offset, either to the side or above the driving plane, or placed at an angle, either panning about the z-axis or tilting about the x-axis, the trigonometric formulas become somewhat more complex. This can be solved either by adding the required trigonometric functions to the formulas and calculating them for every image frame, or by providing separate lookup tables from all camera viewing angles used. In Section 18.5 this method is applied to robot soccer.

17.9 References

BÄSSMANN, H., BESSLICH, P. Ad Oculos: Digital Image Processing, International Thompson Publishing, Washington DC, 1995

BLAKE, A., YUILLE, A. (Eds.) Active Vision, MIT Press, Cambridge MA, 1992

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References

BRÄUNL, T. Parallel Image Processing, Springer-Verlag, Berlin Heidelberg, 2001

BRÄUNL, T. Improv – Image Processing for Robot Vision, http://robotics. ee.uwa.edu.au/improv, 2006

CHO, H., LEE., J.-J. (Ed.) 2002 FIRA Robot World Congress, Proceedings, Korean Robot Soccer Association, Seoul, May 2002

FAUGERAS, O. Three-Dimensional Computer Vision, MIT Press, Cambridge MA, 1993

GONZALES, R., WOODS, R., Digital Image Processing, 2nd Ed., Prentice Hall, Upper Saddle River NJ, 2002

HEARN, D., BAKER, M. Computer Graphics - C Version, Prentice Hall, Upper Saddle River NJ, 1997

KAMINKA, G. LIMA, P., ROJAS, R. (Eds.) RoboCup 2002: Robot Soccer World Cup VI, Proccedings, Fukuoka, Japan, Springer-Verlag, Berlin Heidelberg, 2002

KLETTE, R., PELEG, S., SOMMER, G. (Eds.) Robot Vision, Proceedings of the International Workshop RobVis 2001, Auckland NZ, Lecture Notes in Computer Science, no. 1998, Springer-Verlag, Berlin Heidelberg, Feb. 2001

KORTENKAMP, D., NOURBAKHSH, I., HINKLE, D. The 1996 AAAI Mobile Robot

Competition and Exhibition, AI Magazine, vol. 18, no. 1, 1997, pp. 2532 (8)

LECLERCQ, P., BRÄUNL, T. A Color Segmentation Algorithm for Real-Time Object Localization on Small Embedded Systems, Robot Vision 2001, International Workshop, Auckland NZ, Lecture Notes in Computer Science, no. 1998, Springer-Verlag, Berlin Heidelberg, Feb. 2001, pp. 69-76 (8)

NALWA, V. A Guided Tour of Computer Vision, Addison-Wesley, Reading MA, 1993

PARKER, J. Algorithms for Image Processing and Computer Vision, John Wiley & Sons, New York NY, 1997

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Football, or soccer as it is called in some countries, is often referred to as “the world game”. No other sport is played and followed by as many nations around the world. So it did not take long to establish the idea of robots playing soccer against each other. As has been described earlier on the Micro Mouse Contest, robot competitions are a great opportunity to share new

ideas and actually see good concepts at work.

Robot soccer is more than one robot generation beyond simpler competitions like solving a maze. In soccer, not only do we have a lack of environment structure (less walls), but we now have teams of robots playing an opposing team, involving moving targets (ball and other players), requiring planning, tactics, and strategy – all in real time. So, obviously, this opens up a whole new dimension of problem categories. Robot soccer will remain a great challenge for years to come.

18.1 RoboCup and FIRA Competitions

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264

RoboCup and FIRA Competitions

will change the environment (for example kick the ball) and thereby render one’s own action plans useless if too slow.

One of the frequent disappointments of robot competitions is that enormous research efforts are reduced to “show performance” in a particular event and cannot be appreciated adequately. Adapting from the home lab environment to the competition environment turns out to be quite tricky, and many programs are not as robust as their authors had hoped. On the other hand, the actual competitions are only one part of the event. Most competitions are part of conferences and encourage participants to present the research behind their competition entries, giving them the right forum to discuss related ideas.

Mobile robot competitions brought progress to the field by inspiring people and by continuously pushing the limits of what is possible. Through robot competitions, progress has been achieved in mechanics, electronics, and algorithms [Bräunl 1999].

CIIPS Glory with local vision on each robot

Note the colored patches on top of the Lilliputs players. They need them to determine each robot’s position and orientation with global vision.

Figure 18.1: CIIPS Glory line-up and in play vs. Lilliputs (1998)

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18 Robot Soccer

18.2 Team Structure

The CIIPS Glory robot soccer team (Figure 18.1) consists of four field players and one goal keeper robot [Bräunl, Graf 1999], [Bräunl, Graf 2000]. A local intelligence approach has been implemented, where no global sensing or control system is used. Each field player is equipped with the same control software, only the goal keeper – due to its individual design and task – runs a different program.

Different roles (left/right defender, left/right attacker) are assigned to the four field players. Since the robots have a rather limited field of view with their local cameras, it is important that they are always spread around the whole field. Therefore, each player’s role is linked to a specific area of the field. When the ball is detected in a certain position, only the robot responsible for this area is meant to drive toward and play the ball. The robot which has detected the ball communicates the position of the ball to its team mates which try to find favorable positions on the field to be prepared to take over and play the ball as soon as it enters their area.

Situations might occur when no robot sees the ball. In that case, all robots patrol along specific paths in their assigned area of the field, trying to detect the ball. The goal keeper usually stays in the middle of the goal and only moves once it has detected the ball in a reasonably close position (Figure 18.2).

y

x

Figure 18.2: Robot patrolling motion

This approach appears to be quite efficient, especially since each robot acts individually and does not depend on any global sensing or communication system. For example, the communication system can be switched off without any major effects; the players are still able to continue playing individually.

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Mechanics and Actuators

18.3 Mechanics and Actuators

According to the RoboCup Small-Size League and FIRA RoboSot regulations the size of the SoccerBots has been restricted to 10cm by 15cm. The height is also limited, therefore the EyeCon controller is mounted on a mobile platform at an angle. To catch the ball, the robot has a curved front. The size of the curved area has been calculated from the rule that at least two-thirds of the ball’s projected area must be outside the convex hull around the robot. With the ball having a diameter of approximately 4.5cm, the depth of the curved front must be no more than 1.5cm.

The robots are equipped with two motorized wheels plus two casters at the front and back of the vehicle. Each wheel is controlled separately, which enables the robots to drive forward, backward, as well as drive in curves or spin on the spot. This ability for quick movement changes is necessary to navigate successfully in rapidly changing environments such as during robot soccer competitions.

Two additional servo motors are used to activate a kicking device at the front of the robot and the movement of the on-board camera.

In addition to the four field players of the team, one slightly differing goal keeper robot has been constructed. To enable it to defend the goal successfully it must be able to drive sideways in front of the goal, but look and kick forward. For this purpose, the top plate of the robot is mounted at a 90° angle to the bottom plate. For optimal performance at the competition, the kicking device has been enlarged to the maximum allowed size of 18cm.

18.4 Sensing

Sensing a robot’s environment is the most important part for most mobile robot applications, including robot soccer. We make use of the following sensors:

•Shaft encoders

•Infrared distance measurement sensors

•Compass module

•Digital camera

In addition, we use communication between the robots, which is another source of information input for each robot. Figure 18.3 shows the main sensors of a wheeled SoccerBot in detail.

Shaft encoders The most basic feedback is generated by the motors’ encapsulated shaft encoders. This data is used for three purposes:

•PI controller for individual wheel to maintain constant wheel speed.

•PI controller to maintain desired path curvature (i.e. straight line).

•Dead reckoning to update vehicle position and orientation.

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18 Robot Soccer

The controller’s dedicated timing processor unit (TPU) is used to deal with the shaft encoder feedback as a background process.

Figure 18.3: Sensors: shaft encoder, infrared sensors, digital camera

•Avoid collision with an obstacle.

•Navigate and map an unknown environment.

•Update internal position in a known environment.

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