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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