This document discusses training a CRS robotic arm using human arm imitation through the Kinect sensor. It describes how the Kinect can track human joints in 3D space using its skeletal API. The robotic arm has 6 degrees of freedom and the Denavit-Hartenberg parameters will be used to define reference frames for each link. Joint angles will be calculated using inverse kinematics and transmitted to the robot via serial communication. Local optimization can remove redundant states from the motion path to efficiently perform tasks.

1. Human Arm Tracking
TRAINING A CRS ROBOTIC ARM USING HUMAN ARM
IMITATION THROUGH KINECT

2. MICROSO It consists of:
FT • RGB Camera.
• Two Infrared sensors which act as a depth sensor.
KINECT • Four element microphone array.
SENSOR
In default range mode, Kinect can see people standing between
0.8 meters and 4.0 meters away.
Users will have to be able to use their arms at that distance,
which narrows down to a practical range of 1.2 to 3.5 meters.
In the near mode, Kinect has a practical range of 0.8 to 2.5
meters.
[1]

3. The robotic arm has 6 degree of freedoms:
CRS
ROBOTIC
ARM
[3]
We wouldn’t be using JOINT 6 as the human arm doesn’t has this
degree of freedom.
[2]

4. D-H Parameters
The Denavit–Hartenberg parameters (also called DH parameters) are the four parameters associated with
a particular convention for attaching reference frames to the links of a spatial kinematic chain.
θi - the angle between the axes, Xi-1 and Xi,
about the axis Zi .
di - the distance between Xi-1 and Xi along Zi.
ai - the distance between the common normals
to axes Zi and Zi + 1 along Xi.
αi - the angle between the axes, Zi and Zi + 1, about
the axis Xi.
[4]
Using the above 4 parameters we could define a reference for each link of our robotic arm.

5. Kinect Skeletal API
Microsoft Kinect sensor provides us with the API to track a human body. The Kinect
keeps track of the joints of our arm.
We are using this to provide us with the 3D coordinate of the joints which we
would be further using to calculate the joint angles using the known D-H
parameters of the arm.
[5]

6. Calculating Joint Angles
• We obtain the upper extremity joint position measurement from the kinect sensor, ex., [x y z].
• To calculate joint angles at the 6-DOFs robot manipulator given the end-effector position
measurement, we apply the iterative Newton method for solving the inverse kinematics
problem.
• The algorithm can be described by

7. Transmitting Data To The
Robot
• This will be one of the major challenges.
• The robot is currently controlled by either
a manual controller or using a windows
application RobCoMM (the OS of the
robot)
• The Robot was discontinued in around
1995
• We do not have much info on how to
control the bot in realtime by sending
commands through the serial port
• The data is transmitted to the robot
through Serial Communication
[6]

8. Local Optimization and
Application
Each of the configuration of the robotic arm is a point in a 5-dimensional space.
While we operate the robotic arm using our hand, it is possible that we might have not taken
the optimal path from start to end configuration .
So we could remove some redundant states from our path to optimize our path.
Algorithm: We would connect each state with it’s k nearest neighbors. Then in the new graph
we would find the shortest path between the start and the end state using the Dijkastra’s
Algorithm. After that we would only keep the states found on the above path and discard the
other states.
Thus we can now use the robot to efficiently perform the task any number of times.

9. References
• [1] http://gamesforkinect.org/kinect-information/how-does-the-kinect-sensors-work/
• [2] http://cmp.felk.cvut.cz/cmp/hardware/A465/A465.html
• [3] http://cmp.felk.cvut.cz/cmp/courses/ROB/labsmaterial/CRS/CRS-uvod.htm
• [4] Figure 3.4, Page 66 Introduction to Robotics By John J. Craig.
• [5] http://msdn.microsoft.com/en-us/library/hh973074.aspx
• [6] http://www.doom9.org/index.html?/DigiTV/dbox-howto.htm
• Real-Time Human Pose Recognition in Parts from Single Depth Images
http://research.microsoft.com/pubs/145347/BodyPartRecognition.pdf
• Introduction to Robotics By John J. Craig.

10. Thank You
AYUSH VARSHNEY
RITESH GAUTAM