Fuzzy based decision making for the discrete-continuous control of humanoid robots

Within the next years a new generation of humanoid robots able to manage autonomously sophisticated tasks in a complex, time varying domestic and public environment is going to be developed. To cope with these advanced requirements a new multi-sensor based discrete- continuous supervisory control concept is proposed, which is able to accomplish even complex human skills. Each skill is divided into a sequence of elementary actions (so called Primitive Skills). Depending on the multi-sensor perception of the current state of the system, the discrete control has to provide an optimal selection and activation of the appropriate sequence of action and control strategy. An on-line decision making algorithm based on the structure of Primitive Skills (PS) has been implemented. On a lower level the continuous control has to assure that each PS is performed by means of the most appropriate sub-controllers. The theoretical approach and first experimental results of the ongoing research are presented in this paper.