Image Processing-Based Object Recognition and Manipulation with a 5-DOF Smart Robotic Arm through a Smartphone Interface Using Human

Intent Sensing

Haiming Ganghg1169@nyu.edu

Background

• Some of manipulator work followed by fixed built-in command• Some of manipulator work followed by people command• Need lots of sensors or marks • Low intelligence• Difficult to participate the daily life of human

Solution

• Adjustable manipulator Adjust itself according to various requirements and environment

• Easy using manipulatorLess sensors and marks

• Smart manipulator• Human partner

Help people finish some tasks according to daily activity data of people

System diagram

Structure of arm robot

Base

Shoulder pan joint

Shoulder lift joint

Elbow joint

Wrist joint

Finger

How it work?1. Mobile phone or tablet receives image data from camera

according to target object2. Mobile phone or tablet deals with image frame by frame3. Mobile phone or tablet sends position information of target

object and robot4. Raspberry pi solves inverse kinematic equation to get joint

angular and sends these data to Arbotix-M microcontroller5. Arbotix-M covert joint angular to electrical signal and operate

robot to pick up target object6. Place target object to a fixed and preset position

Mobile phone or tablet receives image data from camera according to target object• Camera connect with raspberry pi by usb

cable and normally open• Raspberry pi sends image frame to

mobile phone via mjpg-streamer (Wireless Transmission)

• Choose object by clicking button in the interface or mobile phone chooses preset object automatically according to pace of running (Step count algorithm for iphone 5 and motion coprocessor for newer version)

Mobile phone or tablet deals with image frame by frame• Object recognition by haar feature classifier (10 FPS)• Obtain the position information between target object and

manipulator (1 FPS)• The position information of mark can be saved in phone, so the

phone does not need to obtain this data every time when using

Box Beer Toothpaste

Pump Cup

Haar cascadeInitially, the algorithm needs a lot of positive images (images of faces) and negative images (images without faces) to train the classifier. Then we need to extract features from it. For this, haar features shown in below image are used. They are just like convolutional kernel. Each feature is a single value obtained by subtracting sum of pixels under white rectangle from sum of pixels under black rectangle.

For example, consider the image below. Top row shows two good features. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. The second feature selected relies on the property that the eyes are darker than the bridge of the nose. The haar feature reflects the change of the gray scalar of the image.

Haar Cascade ClassifierPositive image

Negative image

Box 700 3500Beer 550 2750Toothpaste 700 3500Pump 700 3500Cup 450 2250

Distinguish Condition

(a) (b)

(c) (d)

(e)

Box:B>G>RBeer:G>B, G>R, G<40 and B>30Toothpaste boxR>G>B PumpR>G, R>B and R<27 CupR>B>G

Obtain position information1. Features2D + Homography to find mark2. Houghline function to find the lines’ equations and use the equations to

calculate the position of intersection points3. Affine Transformation to get the birds’-eye-view and calculate the pixel

distance between mark and object

Affine transformation

• Perspective Transform• Distance measurement• Fixed height of object

Robot Mark

Bottom of ObjectGlobal Coordinate

y

x

Publish the position information to the robot by the ios devices

Improvement

• Faster image process speed• More accurate recognition algorithm • More functions combined with mobile phonea) Set an alarm to pick up preset objectb) Utilize habits of people to determine different target objects

in different time

Demo

Thank you!&Question?