This paper considers the robot navigation task as an algorithmic and representational one in a way that the performance of the navigation task cannot be measured without combining those two elements. As a result of this view, a unique new navigation strategy based on combined multi-fusion planning algorithms and multi-paradigm representation schemes is presented. An overall architecture for a new navigation strategy is also proposed. Discrete and continuous planning algorithms are combined in a hierarchal fashion. GIS models and ontology are also combined to form rich media for representing dynamic data and knowledge. Experimental results with an evaluation of the schemes are presented.
|Number of pages||10|
|Journal||Robotics and Autonomous Systems|
|Early online date||1 Aug 2017|
|Publication status||Published - Oct 2017|
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Matthew Walter Dunnigan
- School of Engineering & Physical Sciences - Associate Professor
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Associate Professor
Person: Academic (Research & Teaching)