Abstract
Mobile robot navigation in an unknown world is the principal subject of this
research. In this world we have to supply the mobile robot with the appropriate
sensing equipment for receiving information from the world and decoding it.
The thesis represents knowledge as a probabilistic model of sensing system actions.
Our research combines theoretical knowledge representation models, of uncertainty
with implementation problems.
The central items in the thesisi are sensor-fusion problems, orientation in uncertain
enviroment and mapping the obstacles in the world.
Robot navigation in an unknown world with sonars is formalized as an uncertainty problem.
The mapping model allows for solving the problem in reasonable computation time.
We reduce the number of states that are necessary for the world representation, by dividing
the world into zones, and implement this with a grid, and build a probabilistic network
based on the zones.
Our model emphasizes the dependence between the zones. The model's architecture is
implemented by a computer program.
Initial testing, in different configurations, demonstrates the advantage of our model in
contrast with another models with independence between the zones
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