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09 Jul 2017
Kimon Valavanis, University of Denver, USA
Abstract: When dealing with navigation/control of (semi-) autonomous robotic vehicles in obstacle filled dynamic environments, Fuzzy Logic offers a reliable and viable alternative to conventional controller design and analytic techniques, as it is capable of handling environment uncertainty that is difficult if not impossible to model, as well as system modeling uncertainties without affecting system robustness nor adversely impacting performance. This talk presents a generalized Fuzzy Logic based hierarchical architecture and framework along with its application specific modifications for aerial, aquatic and terrestrial robotic vehicle sensor-based autonomous navigation and control. For such applications, a mathematical model of the dynamics of the vehicle is not needed during the design process of the motion controller; however, the problem-specific heuristic control knowledge is needed for the inference engine design. From the practical and implementation point of view, it is shown that Fuzzy Logic is the most appropriate modeling tool to represent imprecision and uncertainty of sensor readings, and for hardware implementation of fuzzy controllers in real- time due to low computation time. Experimental and simulation studies and results validate and support implemented techniques and approaches to ground, aerial and underwater vehicles, followed by a comparative study of classical and soft computing based controllers designed to control small unmanned helicopters.
Abstract: When dealing with navigation/control of (semi-) autonomous robotic vehicles in obstacle filled dynamic environments, Fuzzy Logic offers a reliable and viable alternative to conventional controller design and analytic techniques, as it is capable of handling environment uncertainty that is difficult if not impossible to model, as well as system modeling uncertainties without affecting system robustness nor adversely impacting performance. This talk presents a generalized Fuzzy Logic based hierarchical architecture and framework along with its application specific modifications for aerial, aquatic and terrestrial robotic vehicle sensor-based autonomous navigation and control. For such applications, a mathematical model of the dynamics of the vehicle is not needed during the design process of the motion controller; however, the problem-specific heuristic control knowledge is needed for the inference engine design. From the practical and implementation point of view, it is shown that Fuzzy Logic is the most appropriate modeling tool to represent imprecision and uncertainty of sensor readings, and for hardware implementation of fuzzy controllers in real- time due to low computation time. Experimental and simulation studies and results validate and support implemented techniques and approaches to ground, aerial and underwater vehicles, followed by a comparative study of classical and soft computing based controllers designed to control small unmanned helicopters.
