Diego – the humanoid biped

Installing the new Roboard processing unit allowed much more precise and sophisticated servo movements than the TI micro-controller did before. Thanks to this the robot was able to complete this step:

Next, the software part of saving and loading these steps needs to be improved to facilitate the process o developing new repeatable walking patterns.

After proving that the robot’s arm articulation is precise enough and that the vision system works well in finding and tracking a ball, I decided to take it a step further and combine sensing and actuating. Actually a few steps further, but before explaining what are all the steps the robot does, let’s see the video:

The robot performs the following steps in this video:

1. Speech recognition of all commands (SAPI 5.1)
2. Looking for the red ball with it’s stereoscopic cameras.
3. Finding the ball’s image and stereo-matching it using the sum of absolute differences algorithm.
4. Calculating the distance of the ball using triangulation.
5. Calculating the Cartesian (x,y,z) position of the ball using trigonometry.
6. Performing inverse kinematics (IK) to determine joint angles to get to the ball.
7. Actuating the servos to take the ball.

Let’s focus on some of the new algorithms.

The red ball is found in the left image by locating the “most red” area in it. Pure red is found in the image by the following formula calculated for each pixel:

diff(x,y)=red(x,y)-blue(x,y)-green(x,y)

the ‘diff’ signal will have its highest values for areas which have only the red color component. Then, an adjustable threshold is applied to cut off other color areas.

After this is done, we commence with stereo matching. This is done along so called epipolar lines. Since the cameras’ optical axes in this system are parallel, the epipolar lines are horizontal. This means that the ball’s image will appear in the right image at the same y-coordinate as on the left image, but translated along the x axis.  The search along the x is done using the 2D sum of absolute differences between the left and right images. The area in the right image that most resembles the area in the left image is declared stereo-matched.

Next, knowing the camera focus lengths and positions of the center of the ball in both images we can calculate the angles under which the cameras see the object. By also knowing the inter-camera distance, we can triangulate the distance of the object.

After the direct line distance is calculated, trigonometry is used to figure out the Cartesian distances in the x,y and z directions. These are fairly complex calculations, since the head’s tilt and yaw angles must be accounted for too.

Knowing the x,y and z coordinates of the ball compared to the origin of the hand in the shoulder, we need to figure out the angles in the arm’s joints to reach the object. This problem is called inverse kinematics. It is an iterative process in which the angles are adjusted until the hand reaches the ball.

Finally, when the angles are calculated the robot performs these and grabs the ball.

The new servos allowed a fully assembled Diego to make its next half-step:

It can be seen that even though the robot can perform a half-step it could need some more torque. It still seems to be a bit too heavy for the servos to support. Diego has a very high center of mass which makes it hard to balance on one foot. To improve dynamics I decided to reduce its weight by taking out the batteries from the torso and temporarily connecting them with wires to the robot. The videos featuring the reduced mass robot will be shown in the following post.

After a night of testing different angle sequences I finally convinced Diego take his first step. It was really amazing, that weeks of planning, building, soldering, programming, etc. seemed finally to pay off:

As it can be noticed, this is actually a half-step, because the robot starts from its standing position.It is also not the nicest motion, but for the first try, it’s ok. Hopefully its next steps will be much more sophisticated.

While experimenting with sequences, I figured out that this is a very lengthy process when using the trial/error approach. Therefore I decided to come up with a kinematics simulation, where I could easily change the angles and see how the robot would behave. You can read more about this in my next post.