This paper presents a hierarchical grey-fuzzy motion decision-making (HGFMD) algorithm, which is capable of integrating multiple sequential data for decision making and for the design of the control kernel of the target tracking system. The algorithm combines multiple grey prediction modules, each of which can estimate a suitable model from sequential sensory information for approximating the observed dynamic system for future-trend prediction and for decision making through a multilayered fuzzy logic inference engine. We have designed the HGFMD controller for a target tracking system and implemented it in our autonomous mobile robot "Chung Cheng-1," The HGFMD is compared with the conventional fuzzy logic controller, multilayered fuzzy controller, and the original grey-fuzzy controller we have developed previously in various target-tracking experiments, We have demonstrated the high reliability of the HGFMD controller and tracking system even when encountering the uncertain status of slow sensory response time and the nonlinear motion behaviors of the target. The HGFMD controller can be considered as a systematical and modularized means to design decision kernel of systems with multiple sensors, Furthermore, the decision-making kernel implemented herein based on HGFMD algorithm is suitable for distributed processing systems.
