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    SEMINAR REPORT 2012 COMMUNICATION ROBOT SYSTEM BASED ON THE

    HANDSHAKING ACTION

    DEPARTMENT OF E&I Page 1 VJEC,CHEMPERI

    CHAPTER 1

    INTRODUCTION

    The development of robots has been significant in production, including factories. The expectation

    is high for the development of intelligent robot systems that work cooperatively with human beings in

    daily life and in medical treatment and welfare. Human robot interaction is essential for the operation of

    robots by people. Anyone can operate robots with ease by giving commands to the robot using gestures,

    just as people communicate with gestures. An intelligent manipulator system using gesture recognition has

    been developed. The omnidirectional image is used for the robot control system based on hand gestures

    The communication robot system based on stereo vision and voice instructions was developed. The

    control algorithm for a service robot through the hand over task has been proposed. This paper discussed a

    human robot interaction based on the handshaking action. We developed a communication robot

    HAKUEN that is composed of a multimedia robot with stereo camera, a wheel type mobile robot and a

    PC with a microphone. The HAKUEN approaches and holds out its hand toward the operator according to

    the voice command. The HAKUEN detects the operator's face based on the pixel values of the flesh tint in

    the color image. We use the disparity in order to calculate the distance between the robot and the operator.

    The effectiveness of our system is clarified by several experimental results

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    SEMINAR REPORT 2012 COMMUNICATION ROBOT SYSTEM BASED ON THE

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    DEPARTMENT OF E&I Page 2 VJEC,CHEMPERI

    CHAPTER 2

    LITERTURE SURVAY

    UNECE issues its 2004 World Robotics survey

    Worldwide investment in industrial robots up 19% in 2003. In first half of 2004, orders for

    robots were up another 18% to the highest level ever recorded. Worldwide growth in the period 2004-

    2007 forecast at an average annual rate of about 7%. Over 600,000 household robots in use - several

    millions in the next few years. From the above press release we can easily realize that household (service)

    robots getting popular. This gives the researcher more interest to work with service robots to make it

    more user friendly to the social context. Speech Recognition (SR) technology gives the researcher the

    opportunity to add Natural language (NL) communication with robot in natural and even way in the social

    context. So the promise of robot that behave more similar to humans (at least from the perception-

    response point of view) is starting to become a reality [28]. Brooks research [5] is also an example of

    developing humanoid robot and raised some research issues. Form these issues; one of the important

    issues is to develop machine that have human-like perception.

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    SEMINAR REPORT 2012 COMMUNICATION ROBOT SYSTEM BASED ON THE

    HANDSHAKING ACTION

    DEPARTMENT OF E&I Page 3 VJEC,CHEMPERI

    CHAPTER 3

    ABOUT ROBOT

    The term robot generally connotes some anthropomorphic (human-like) appearance consider

    robot arms for welding . The main goal robotic is to make Robot workers, which can smart enough to

    replace human from labor work or any kind of dangerous task that can be harmful for human. The idea of

    robot made up mechanical parts came from the science fiction. Three classical films, Metropolis (1926),

    The Day the Earth Stood Still (1951), and Forbidden Planet (1956), cemented the connotation that robots

    were mechanical in origin, ignoring the biological origins in Capeks play. To work as a replacement of

    human robot need some Intelligence to do function autonomously. AI (Artificial intelligence) gives us the

    opportunity to fulfill the intelligent requirement in robotics. There are three paradigms are followed in AI

    robotics depends on the problems. These are - Hierarchical, Reactive, and Hybrid deliberative/reactive.

    Applying the right paradigm makes problem solving easier . Depending on three commonly acceptedrobotic primitives the overview of three paradigms of robotics on Figure 2.1.

    In our project we follow Hybrid reactive paradigm to solve our robotic

    Fig 3.1: Three paradigms a) Hierachical b) Reactive c) Hybrid reactive

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    CHAPTER 4

    ROBOT CONSTRUCTION

    We developed a communication robot HAKUEN that is shown in Figure1. This system is

    composed of a multimedia robot with stereo camera, a wheel type mobile robot and a PC with a

    microphone. The HAKUEN has two arms and each arm has six degrees of freedom of motion. The head

    of the multimedia robot has two degrees of freedom of motion. The several LEDs are equipped around the

    robot's eyes. The base of the robot is a two wheels mobile robot. When the operator gives the voice

    command to the HAKUEN, the robot approaches and holds out its hand toward the operator. The

    HAKUEN moves according to the operator's voice commands. We made the four motion functions about

    the HAKUEN. These functions are shown below.

    (1) Face tracking function

    The HAKUEN moves its head in order to follow the operator's face motion. We call the motion is a "face

    tracking function". The operators face is detected based on the pixel values of the flesh tint in the color

    image.

    (2) Handshaking function

    The HAKUEN holds out its right hand toward the operator in order for the operator to shake robots hand.

    We call the motion is a "handshaking function".

    (3) Voice recognition function

    The HAKUEN moves according to the operator's voice commands. We call the motion is a "voice

    recognition function". We use the voice recognition software (via voice, IBM) which is controlled by the

    Active X program in order to recognize the voice commands.

    (4)Approach function

    We consider that the suitable distance range between the HAKUEN and the operator is 0.6[m]-1.2[m].

    The robot approaches the operator and keeps the suitable distance. We call the motion is an approach

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    DEPARTMENT OF E&I Page 5 VJEC,CHEMPERI

    Our assistive robot system is shown in Figure 1. This system is composed of the manipulator, a PC , a

    microphone and a stereo vision hardware. The manipulator used here has six degrees of freedom of

    motion and has a mechanical hand. Since the system has to recognize the position and posture of the hand

    in real time, we use the stereo vision hardware. In our system the operator gives a hand gesture to the

    manipulator conversationally. For example, when the operator points with the forefinger to the object and

    gives the voice instruction to the manipulator in order to indicate the target object, the manipulator picks

    up the object and hands it over to the operator.

    .

    Fig 4.1the Hakuen robot

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    HANDSHAKING ACTION

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    CHAPTER 5

    FACE TRACKING FUCTION

    At first, the HAKUEN has to detect the human face in the color image. The human face is detected based

    on the pixel values of the flesh tint in the color image. The color image is digitized as 24 bit RGB (Red,

    Green and Blue) pixel value, so that each element of RGB is 8 bit or 256 levels of brightness6). However,

    the RGB value is apt to be influenced by the light. Therefore, we use the HLS (hue, saturation, and value)

    color specification system in order to detect the human face accurately. The each elements of HLS color

    specification system are described in (1)-(3) and calculated based on the RGB pixel value. In order to

    detect the human face in the color image, we transform color image to the binary image based on the

    threshold values of HSL color specification system. We define the threshold values of HSL color

    specification system about the flesh tint through the experiment

    The system operates in two stages: it first applies a set of neural network-based filters to an image, and

    then uses an arbitrator to combine the outputs. The filters examine each location in the image at several

    scales, looking for locations that might contain a face. The arbitrator then merges detections from

    individual filters and eliminates overlapping detections.

    The first component of our system is a filter that receives as input a 20x20 pixel region of the image, and

    generates an output ranging from 1 to -1, signifying the presence or absence of a face, respectively. To

    detect faces anywhere in the input, the filter is applied at every location in the image. To detect faces

    larger than the window size, the input image is repeatedly reduced in size (by subsampling), and the filter

    is applied at each size. This filter must have some invariance to position and scale. The amount of

    invariance determines the number of scales and positions at which it must be applied. For the work

    presented here, we apply the filter at every pixel position in the image, and scale the image down by a

    factor of 1.2 for each step in the pyramid. First, a p