Motion　planning　in　an　unknown　environment　is　a　common　challenge　because　of　the　existing　uncertainties.　Representatively,　the　partially　observable　Markov　decision　process　(POMDP)　is　a　general　mathematical　framework　for　planning　in　uncertain　environments.　Recent　POMDP　solvers　generally　adopt　the　sparse　reward　scheme　to　solve　the　planning　under　uncertainty　problem.　Subsequently,　the　robot＇s　exploration　may　be　hindered　without　immediate　rewards,　resulting　in　excessively　long　planning　time.　In　this　article,　a　POMDP　method,　information　entropy　determinized　sparse　partially　observation　tree　(IE-DESPOT),　is　proposed　to　explore　a　high-quality　solution　and　efficient　planning　in　unknown　environments.　First,　a　novel　sample　method　integrating　state　distribution　and　Gaussian　distribution　is　proposed　to　optimize　the　quality　of　the　sampled　states.　Then,　an　information　entropy　based　on　sampled　states　is　established　for　real-time　reward　calculation,　resulting　in　the　improvement　of　robot　exploration　efficiency.　Moreover,　the　near-optimality　and　convergence　of　the　proposed　algorithm　are　analyzed.　As　a　result,　compared　with　general-purpose　POMDP　solvers,　the　proposed　algorithm　exhibits　fast　convergence　to　a　near-optimal　policy　in　many　examples　of　interest.　Furthermore,　the　IE-DESPOT＇s　performance　is　verified　in　real　mobile　robot　experiments.