A framework for the natural-language-perception-based creative control of unmanned ground vehicles

Mobile robots must often operate in an unstructured environment cluttered with obstacles and with many possible action paths. That is why mobile robotics problems are complex with many unanswered questions. To reach a high degree of autonomous operation, a new level of learning is required. On the one hand, promising learning theories such as the adaptive critic and creative control have been proposed, while on other hand the human brain’s processing ability has amazed and inspired researchers in the area of Unmanned Ground Vehicles but has been difficult to emulate in practice. A new direction in the fuzzy theory tries to develop a theory to deal with the perceptions conveyed by the natural language. This paper tries to combine these two fields and present a framework for autonomous robot navigation. The proposed creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The TCC module utilizes computational theory of perceptions to deal with the high levels of task planning. The authors are currently trying to implement the model on a real mobile robot and the preliminary results have been described in this paper.