Abstract:

A bio-robot system refers to an animal equipped with Brain-Computer Interface (BCI), through which the outer stimulation is delivered directly into the animal’s brain to control its behaviors. The development of bio-robots suffers from the dependency on real-time guidance by human operators. Because of its inherent difficulties, there is no feasible method for automatic con-trolling of bio-robots yet. In this paper, we propose a new method to realize the automatic navigation for bio-robots. A General Regression Neural Network (GRNN) is adopted to analyze and model the controlling procedure of human operations. Com-paring to the traditional approaches with explicit controlling rules, our algorithm learns the controlling process and imitates the decision-making of human-beings to steer the rat-robot automatically. In real-time navigation experiments, our method suc-cessfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future ap-plications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.

Key words: bio-robot, rat-robots, brain-computer interface, automatic navigation