gesture controlled robot application advantage disadvantage
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gesture controlled robot documentation with clear explanation
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gesture controlled robot application advantage disadvantage

INTRODUCTION

Nowadays, robots are used in the different domains ranging from search and rescue in the dangerous environments to the interactive entertainments. The more the robots are employed in our daily life, the more a natural communication with the robot is required. Since the introduction of the most common input computer devices not a lot have changed. This is probably because the existing devices are adequate. It is also now that computers have been so tightly integrated with everyday life, that new applications and hardware are constantly introduced. The means of communicating with computers at the moment are limited to keyboards, mice, light pen, trackball, keypads etc. These devices have grown to be familiar but inherently limit the speed and naturalness with which we interact with the computer. On the other hand, hand gesture, as a natural interface means, has been attracting so much attention for interactive communication with robots in the recent years.

As the computer industry follows Moore’s Law since middle 1960s, powerful machines are built equipped with more peripherals. Vision based interfaces are feasible and at the present moment the computer is able to “see”. Hence users are allowed for richer and user-friendlier man-machine interaction. This can lead to new interfaces that will allow the deployment of new commands that are not possible with the current input devices. Plenty of time will be saved as well. Recently, there has been a surge in interest in recognizing human hand gestures. In this context, vision based hand detection and tracking techniques are used to provide an efficient real time interface with the robot. However, the problem of visual hand recognition and tracking is quite challenging. Many early approaches used position markers or colored gloves to make the problem of hand recognition easier, but due to their inconvenience, they cannot be considered as a natural interface for the robot control. Thanks to the latest advances in the computer vision field, the recent vision based approaches do not need any extra hardware except a camera. These techniques can be categorized as model based and appearance based methods. While model based techniques can recognize the hand motion and its shape exactly, they are computationally expensive and therefore they are infeasible for a real time control application.
The appearance based techniques on the other hand are faster but they still deal with some issues such as:

complex nature of the hand with more than 20 DOF
cluttered and variant background
variation in lighting conditions
real time computational demand

This project on the one hand addresses the solution to the mentioned issues in the hand recognition problem using 2D images and on the other hand proposes an innovative natural commanding system for a Human Robot Interaction (HRI).Hand- gesture recognition has various applications like computer games, machinery control (e.g. Crane), and thorough mouse replacement. One of the most structured sets of gestures belongs to sign language. In sign language, each gesture has an assigned meaning (or meanings).Computer recognition of hand gestures may provide a more natural-computer interface, allowing people to point, or rotate a CAD model by rotating their hands. Hand gestures can be classified in two categories: static and dynamic. A static gesture is a particular hand configuration and pose, represented by a single image. A dynamic gesture is a moving gesture, represented by a sequence of images. We will focus on the recognition of static images. Interactive applications pose particular challenges. The response time should be very fast. The user should sense no appreciable delay between when he or she makes a gesture motion and when the computer responds. The computer vision algorithms should be reliable and work for different people. There are also economic constraints: the vision-based interfaces will be replacing existing ones, which are often very low cost. Even for added functionality, consumers may not want to spend more. When additional hardware is needed the cost is considerable higher.
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