Intelligent Senses in Robot Based on Neural Networks

This Paper describes a model and an implementation of spiking neurons for embedded microcontrollers with few bytes of memory and very low power consumption. The proposed model consists of an elementary neuron network that used Hebbian Learning to train a robot to respond to the environment implementing Artificial Intelligence (AI) in robot. The model is implemented using ATMEGA8 Microcontroller based on AVR RISC Architecture and tested with an ability to move forward and Backward according to intensity of light without human intervention and external computers. I. I NTRODUCTION Now days, automation is applied in every industry. The human labor is being replaced by robots, ex. CNC machines and PLC. The time is demanding more advancement in every sector of life. AI based on speech and image processing has been developed at University of Emirates, UAE. The interest of man is increasing in AI and it has got no limits, weather it is replicating all the features of a human being or making a super human. In the recent past, with the improvement of the technologies associated with computing and robots, there has been a broad based attempt to build embodied intelligences. But the peculiar nature of this field has resulted in the many attempts being almost entirely unconnected. Because of the difficulty and lack of success in building physical robots, there has been a tendency towards computer simulation, termed "Artificial General Intelligence" where virtual agents in a virtual reality world attempt to achieve intelligent behavior. By the 1980's AI researchers were beginning to understand that creating artificial intelligence was a lot more complicated than first thought. Given this, Brooks came to believe that the way forward in consciousness was for researchers to focus on creating individual modules based on different aspects of the human brain, such as a planning module, a memorymodule etc., which could later be combined