Motion　planning　in　uncertain　environments　is　a　common　challenge　and　essential　for　autonomous　robot　operations.　Representatively,　the　determinized　sparse　partially　observable　tree　(DESPOT)　algorithm　shows　reasonable　performance　for　planning　under　uncertainty.　However,　DESPOT　may　generate　a　low-quality　solution　due　to　inaccurate　searches　and　low　efficiencies　in　the　belief　tree　construction.　Therefore,　this　paper　proposes　a　high-efficiency　online　planning　method　built　upon　the　DESPOT　algorithm,　namely,　the　DESPOT　with　discounted　upper　and　lower　bounds　(DESPOT-DULB)　algorithm,　to　simultaneously　improve　the　efficiency　and　performance　of　motion　planning.　Particularly,　the　node＇s　information　is　represented　by　combining　the　upper　and　lower　bounds　of　the　node　(ULB)　in　the　forward　exploration　of　the　action　space　to　reasonably　assist　the　optimal　action　selection.　Then,　a　discounted　factor　based　on　the　depth　information　of　the　belief　tree　is　introduced　to　reduce　the　gap　between　the　upper　bound　and　lower　bound　both　in　the　action　space　and　observation　space.　As　a　result,　the　proposed　method　can　comprehensively　represent　the　information　of　the　node　to　ensure　a　near-optimal　forward　search.　The　theoretical　proofs　of　the　proposed　method　are　provided　as　well.　The　simulation　results,　including　three　representative　scenario　comparisons　and　a　parameter　sensitivity　analysis,　demonstrate　that　the　proposed　method　exhibits　favorable　performances　in　many　examples　of　interest.