Type of Document Master's Thesis Author Gauss, Veronica A. URN etd-08222008-063102 Title A fuzzy logic solution for navigation of the Subsurface Explorer planetary exploration robot Degree Master of Science Department Electrical Engineering Advisory Committee
Advisor Name Title Bay, John S. Committee Chair Abbott, A. Lynn Committee Member VanLandingham, Hugh F. Committee Member Keywords
- fuzzy logic
- mobile robots
- unsupervised learning
- Subsurface Explorer
- Mars exploration
Date of Defense 1997-05-15 Availability restricted Abstract
An unsupervised fuzzy logic navigation algorithm is designed and implemented in simulation for the Subsurface Explorer planetary exploration robot. The robot is intended for the subterranean exploration of Mars, and will be equipped with acoustic sensing for detecting obstacles. Measurements of obstacle distance and direction are anticipated to be imprecise however, since the performance of acoustic sensors is degraded in underground environments. Fuzzy logic is a satisfactory means of addressing imprecision in plant characteristics, and has been implemented in a variety of autonomous vehicle navigation applications. However, most fuzzy logic algorithms that perform well in unknown environments have large rule-bases or use complex methods for tuning fuzzy membership functions and rules. These qualities make them too computationally intensive to be used for planetary exploration robots like the SSX.
In this thesis, we introduce an unsupervised fuzzy logic algorithm that can determine a trajectory for the SSX through unknown environments. This algorithm uses a combination of simple fusion of robot behaviors and self-tuning membership functions to determine robot navigation without resorting to the degree of complexity of previous fuzzy logic algorithms.
Finally, we present some simulation results that demonstrate the practicality of our algorithm in navigating in different environments. The simulations justify the use of our fuzzy logic technique, and suggest future areas of research for fuzzy logic navigation algorithms.
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