Emili Hernández Bes
This thesis addresses the path planning problem for Autonomous Underwater Vehicles (AUVs) using homotopy classes to provide topological information on how paths avoid obstacles. Looking for a path within a homotopy class constrains the search into a specific area of the search space, speeding up the computation of the path. Given a workspace with obstacles, the method starts by generating the homotopy classes. Those which probably contain lower cost solutions are determined by means of a lower bound criterion before computing a path. Finally, a path planner uses the topological information of homotopy classes to generate a few good solutions very quickly. Three path planners from different approaches have been proposed to generate paths for the homotopy classes obtained. The path planning is performed on Occupancy Grid Maps (OGMs) improved with probabilistic scan matching techniques. The results obtained with synthetic s scenarios and with real datasets show the feasibility of our method.
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