Navigating　through　complex　flow　fields,　underwater　vehicles　often　face　insufficient　thrust　to　traverse　particularly　strong　current　areas,　necessitating　consideration　of　the　physical　feasibility　of　paths　during　route　planning.　By　constructing　a　flow　field　database　through　Computational　Fluid　Dynamics　(CFD)　simulations　of　the　operational　environment,　we　were　able　to　analyze　local　uncertainties　within　the　flow　field.　Our　investigation　into　path　planning　using　these　flow　field　data　has　led　to　the　proposal　of　a　hierarchical　planning　strategy　that　integrates　global　sampling　with　local　optimization,　ensuring　both　completeness　and　optimality　of　the　planner.　Initially,　we　developed　an　improved　global　sampling　algorithm　derived　from　RRT　to　attain　nearly　optimal　theoretical　feasible　solutions　on　a　global　scale.　Subsequently,　we　implemented　corrective　measures　using　directed　expansion　to　address　locally　infeasible　sections.　The　algorithm＇s　efficacy　was　theoretically　validated,　and　simulated　results　based　on　real　flow　field　environments　were　provided.