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Humanoid Robots Used for Surveillance

PostPosted: Wed Sep 11, 2013 6:39 pm
by Prasanth
Today is a world of new scientific invention. As this paper includes the use of humanoid robots in the field of security. There is a wide range of use of robot like in restaurant, in spying; in medical .our main objective is to bring this humanoid robot in the field of surveillance. My paper will give immense use to the army personnel those who are survellancing in the remote areas of the India. Now question arises why only humanoid robots used for the surveillance purpose? Now for this answer take the example of Mumbai terror attack the terrorist have done the damage for 72 the N.S.G doesn’t know how many terrorist are there in hotel Taj .for this reason the robots are designed in a such a way to counter such terror activities . In my paper I have presented some modification in the robot i.e. to use the metal detector, infrared cameras for sensing in the very dim light or no light .temperature detector for detecting the human inside the building. In my paper I have used 10 mega pixel camera 360 rotation to get the clear cut view of the inside building .so this will help the army personnel to counter strike with the terrorist. There will be fewer casualties to army as well as human being. My motive for bringing this paper to you that there is a great scope of enhancement in the field of robotics for the security inside the country. The robot will also carry insurgency operation in country for countering terrorism.

Human-Robot has recently received considerable attention in the academic community, in labs, in technology companies, and through the media. Because of this attention, it is desirable to present a survey of HR to serve as a tutorial to people outside the field and to promote discussion of a unified vision of HR within the field. The goal of this review is to present a unified treatment of HR-related problems, to identify key themes, and discuss challenge problems that are likely to shape the field in the near future. Although the review follows a survey structure, the goal of presenting a coherent "story" of HR means that there are necessarily some well-written, intriguing, and influential papers that are not referenced. Instead of trying to survey every paper, we describe the HR story from multiple perspectives with an eye toward identifying themes that cross applications. The survey attempts to include papers that represent a fair cross section of the universities, government efforts, industry labs, and countries that contribute to HR, and a cross section of the disciplines that contribute to the field, such as human, factors, robotics, cognitive psychology, and design. in the field of defense it is widely used for searching and surveillance purpose because it decreases the human casualties and other hindrance for the army personnel.


Scientists at Dublin City University have researched a subset of human movements called bimanual movements. At different stages of this research they have approached the problems from the novel points of view. They believe that many machine learning problems can accommodate neuroscience and perceptual aspects of human movements for learning and recognizing human behaviors.Learning and recognizing human movements have been given great attention of researchers around the world in the recent years. A broad range of applications from medicine to surveillance and security can benefit from this technology. Learning hand movements and recognizing gestures are significant components of such technologies.

Bimanual movements in general form a large subset of hand movements in which both hands move simultaneously in order to do a task or imply a meaning. Clapping, opening a bottle, typing on a keyboard and drumming are some usual bimanual movements. Sign Languages also use bimanual movements to accommodate sets of gestures for communication.

Due to the involvement of both hands, understanding bimanual movements requires not only computer vision and pattern recognition techniques but also neuroscientific studies as a background to perceive the movements.

A cognitive system for bimanual movements learning and understanding entails three fundamental components (see Figure 1): low-level image processing to deal with sensory data, intelligent hand tracking to recognize the left hand from the right hand, and machine learning for understanding the movements.