With the development of intelligent robotics and information technology, multifarious intelligent machines have come forth. Unfortunately, because their stability and flexibility are not satisfied, these machines are still under tentative schedules. For example, these machines can not be applied in complicated situations, such as searching for survivals from earthquake or bombing area. So some scientists and researchers present the concept of animal robot or hybrid robot in order to overcome the limitations of traditional robotics. Research on animal robotics is becoming a hot topic recently. That is, by carrying electronic devices or sensors, animals are remotely controlled to perform specific tasks and serve as robots. Thus, this will not only provide insights into how animal learns, which may eventually lead to better prosthetics, but also open new possibilities for biomedical, artificial intelligence and cognitive science researches. It also has a practical value in searching for victims from earthquake or bombing area, and the military value can never be overestimated. It is a big problem to make animals perform specific tasks and serve as robots, and there is not an effective method to train animal robots yet. So we designed a platform for animal robots training, which includes both the hardware and the software. After testing many training methods and experimenting different kinds of animals, a valid training method was found and animal robot prototypes were designed. The main contents and achievements of this dissertation are as follows: (1) After analyzing the established animal training methods, we presented a new animal robot training method, which integrated with both sound cue and electrical stimulation. The training results showed that our methods were valid for training animal robots，and rat-robot and rabbit-robot prototypes were trained successfully. (2) A new remote-controlled stimulator was developed, which had the characteristic of compactness and light weight. By setting the stimulation parameters conveniently, it can also be used to cognitive and other researches, and its performance and effectiveness were tested by experiments. (3) An animal tracking system was designed to monitor the animals’ performance after being stimulated. With a digital CCD camera, an image capture card and a personal computer, we developed a fast and robust tracking system, which used a color segmentation technique. The training results showed that the platform worked accurately even under interference conditions. (4) A remote-controlled animal robot training system based on head movements was designed to satisfy the requirements of animal behavior monitoring in outdoor.
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