31-05-2012, 11:40 AM
ARTIFICIAL INTELLIGENCE AND EXTREME ROBOTICS
ARTIFICIAL INTELLIGENCE AND EXTREME ROBOTICS.doc (Size: 470 KB / Downloads: 79)
ABSTRACT:
The paper describes an integrated social intelligence, motion manipulation and natural language processing. The challenge of interacting with humans constrains how our robots appear physically, how they move, how they perceive the world, and how their behaviours are organized .The construction and function of a humanoid, several researches of artificial intelligence in robots regarding kinetics and dynamics of the robotic system regarding the four wheel drive, two wheel drive and human like walking for movement is undertaken briefly. We have discussed how to negotiate between the physical constraints of the robot, the perceptual needs of the robot s behavioural and motivational systems, and the social implications of motor acts. Simple robotic systems based on ability to move on a given path and object detection is explained. Leading robots like ASIMO, KISMET & COCO are taken in consideration.
INTRODUCTION:
For robots and humans to interact meaningfully, it is important that they understand each other enough to be able to shape each other s behaviour. This has several implications. One of the most basic is that robot and human should have at least some overlapping perceptual abilities. Otherwise, they can have little idea of what the other is sensing and responding to. Vision is one important sensory modality for human interaction, and the one we focus on in this article.
SYSTEM ARCHITECTURE:
Our hardware and software control architectures have been designed to meet the challenge of real-time processing of visual signals (approaching 30 Hz) with minimal latencies. Kismet s vision system is implemented on a network of nine 400 MHz commercial PCs running the QNX real-time operating system. Kismet s motivational system runs on a collection of four Motorola 68332 processors.
FUTURE WORK :
With the robot fully assembled, the next step is to develop the software architecture. Some of the vision algorithms for people interaction need also to be developed, as well as most of the navigation algorithms. The vision system should be made adaptable to increased agility, speed, and sensor acquisition of the robot. It may also be possible to implement learning strategies to enhance this flexibility. In addition, a thermal camera was already added to the robot, facilitating person and obstacle detection and enabling night vision. Moreover Our code will need to evolve significantly as we use it to produce intelligent behaviours in Coco. Most of our previous and current work has focused on the design and implementation of appropriate abstractions for affective processing.
. CONCLUSION :
Building affective robots offers a unique opportunity to address scientific questions regarding the nature of affective processing in humans and animals. Given that we have a direct window into the robot’s control systems, it is possible for us to selectively manipulate our models and perform testing in a controlled and repeatable environment. This facilitates the comparison of our implementations with respect to cognitive, ethological, or neurobiological models, thus giving us some insight into the validity of those models and into affective processing in general. Motor control for a social robot poses challenges beyond issues of stability and accuracy.