Fish catching by adopting neural network and chaos to robotic intelligence

In this paper we have dealt with prediction of fish motion under the vision system provided by CCD camera and embedded chaos motion into the system for more effective catching action. Fearing the tracking net attached at robot hand, the fish can suddenly change its escaping trajectory or speed up. Furthermore, as the time of tracking process flows, the fish can somewhat get accustomed to the environment and begin to learn new strategies about how to escape from the bothering net. For example, the fish tends to stay within a corner where it is forbidden for the net to reach for safety or stays away from the net by keeping a constant distance, which can be thought that the fishes know how to produce a steady-state error in a control loop of visual feedback. For the sake of such innate ability being widely existed in animal behavior, the effective intelligent method will need to be conceived to go beyond the fish intelligence. The purpose of this paper is to construct an intelligent system that is able to exceed the fish intelligence in order to track and catch the fish successfully like fish-eating animals in nature to survive.