Mobile　robots　generally　work　in　harsh　and　restricted　environments,　which　poses　challenges　for　mobile　robots　to　find　a　feasible　path　efficiently.　This　article　presents　a　planning　method,　namely　sampling-enhanced　exploration　tree　(SET),　to　improve　computational　efficiency　in　restricted　environments　while　guaranteeing　high-quality　performance.　The　core　of　SET　is　sampling-enhanced　exploration,　which　consists　of　critical　areas　identification,　guiding-exploration,　and　rectifying-exploration.　In　the　critical　areas　identification　phase,　the　restricted　areas　are　identified　based　on　the　distribution　of　the　hybrid　samples.　Next,　the　critical　samples　in　restricted　areas　are　selected　as　the　origins　of　the　sampling-enhanced　exploration.　In　the　guiding-exploration　phase,　the　sampling-enhanced　exploration　starts　from　the　origins　and　marches　quickly　with　the　guidance　of　the　leader-samples　to　capture　the　spatial　feature　and　connectivity　of　the　restricted　areas.　The　spatial　information　provides　essential　guidance　for　efficient　biased　sampling.　In　the　rectifying-exploration　phase,　the　directions　of　sampling-enhanced　exploration　are　rectified　to　transit　the　problematic　areas　and　supplement　samples.　Theoretical　analysis　is　provided　to　shed　light　on　the　properties　of　SET.　Moreover,　the　generality　and　effectiveness　of　SET　are　verified　through　a　series　of　mobile　robot　simulations　and　real-world　experiments.　IEEE