Automatic Navigation for Rat-Robots with Modeling of the Human Guidance

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 controlling 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. Comparing 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 successfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future applications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.