Research on intelligent cognitive function enhancement of intelligent robot based on ant colony algorithm

Abstract

In this paper, ant colony algorithm is studied to improve the visual cognitive function of intelligent robots. Based on the detailed understanding of the research status in this field at home and abroad, and learning from cognitive science and neurobiology research results, a solution is proposed from the perspective of ant colony algorithm based on human brain structure and function. By simulating the process of autonomous learning controlled by human long-term memory and its working memory, a visual strangeness-driven growth long-term memory autonomous learning algorithm is proposed. This method takes incremental self-organizing network as long-term memory structure, and combines with visual strangeness internal motivation Q learning method in working memory. The visual knowledge acquired by self-learning is accumulated into long-term memory continuously, thus realizing the ability of self-learning, memory and intelligence development similar to human beings. The experimental results show that the robot can learn visual knowledge independently, store and update knowledge incrementally, and improve its intelligence development, classification and recognition ability compared with the method without long-term memory. At the same time, the generalization ability and knowledge expansion ability are also improved.