Gait control of hexapod walking robots using mutual-coupled immune networks

Biological information processing systems can be said to be one of the ultimate decentralized systems and have been expected to provide various fruitful ideas to engineering fields, especially robotics. Among these systems, brain-nervous and genetic systems have already been widely used in modeling as neural networks and genetic algorithms, respectively. On the other hand, the immune system also plays an important role in coping with a dynamically changing environment by constructing self-non-self recognition networks among different species of antibodies. This system has many interesting features such as learning, self-organizing abilities, etc., viewed from the engineering standpoint. Therefore, it can be expected to provide novel approaches to the PDP paradigm. However, the immune system has not yet been applied to engineering fields. In this paper, we propose a new hypothesis concerning the structure of the immune system, called the mutual-coupled immune networks hypothesis, based on recent studies on immunology. We apply this idea to gait acquisition of a hexapod walking robot as a practical example. Finally, the feasibility of our proposed method is confirmed by simulations.