The Robot Operating System (ROS) is the main tool used for programming the gesture recognition interface. Here is a graph of all of the nodes:

• openni_tracker: This node is provided by OpenNI as a package called openni_kinect. This node tracks a user using the Xbox Kinect and generates skeleton data.

• kinect_listener: Generates pose data based on the angles of the left hand/arm of the user. This node uses code provided by Cornell’s Personal Robotics research lab.

• gesture_recognizer: Sends requests to the classifier and delegates actions to the robot.

• classifier: Classifies gestures based on pose data sent from gesture_recognizer node.

• add_gesture: Susbcribes to pose data and creates new model file for the classifier to use.

Senior Project – Computer Science – 2013

Developing a Gesture Recognition InterfaceBy – Jonathan Lebron

Advisor – Prof. Nick Webb

The field of robotics presents a unique opportunity to design new technologies that can perform the tasks of humans. This is especially important in cases where a task is too difficult or dangerous, such as war. This project serves to bridge the human-robot communication gap by presenting a robust gesture recognition interface that can recognize gestures and translate these into actions for a robot to perform. The system uses the Xbox Kinect and the Robot Operating System to recognize a set of seven military gestures. The system accurately recognizes complex sequences of these gestures and performs a preset action for each gesture, and has the functionality of adding new gestures or replacing old gestures on the fly.

http://www.ros.org/news/resources/2010/NITE-1.png

There are various scenarios in warfare where speech is not possible or an optimal solution. When presented these situations soldiers have a defined set of gestures they can use to communicate. The question this project looks to solve is: How do you give a robot a sequence of natural gestures that they have to interpret and act upon?

Kinect Skeleton Tracking

Xbox Kinect ™

Improvements to my system include refining the classifier to better interpret gestures. As of now the classifier uses the output of a C4.5 decision tree. Also, improving the tracker so there is no initial calibration necessary will help the system flow more naturally.

Perform Gesture Kinect tracks Skeleton, generates

X,Y,Z coordinates

Calculate angles and classify gesture based on angles, move robot

left_shoulder:0.35,0.12,2.19left_elbow:-0.18,-0.10,2.02left_hand:-0.13,-0.34,1.924

Gesture Recognized!

Now moving!

Classification:

ClassifierGestures

Abreast EnemyFreeze StopListen RifleCover

AngleshandThetahandPhielbowThetaelbowPhi