Extensive　workspace　traversal　by　multiple　robots　may　compromise　planning　efficiency　due　to　over-exploration　and　redundant　calculations　in　inefficient　regions,　while　trying　to　ensure　the　optimality　of　motion　planning　in　multi-robot　systems　(MRS).　This　paper　proposes　a　Voronoi-inspired　random　tree　(VRT*),　to　improve　planning　efficiency　in　MRS　by　mitigating　inefficient　workspace　exploration　and　reducing　redundant　computations.　In　particular,　generalized　Voronoi　graph　(GVG)　is　used　to　transform　workspace　into　searchable　objects.　Subsequently,　a　voronoi　guide　tree　is　constructed　to　capture　the　connectivity　and　optimization　potential　of　workspace,　enabling　the　identification　of　critical　workspace　and　facilitating　the　planning　of　heuristic　paths.　Through　integrating　the　Expansion　direction　selection　strategy　into　a　discrete　rapidly-exploring　random　tree,　optimal　solutions　can　be　achieved　by　VRT∗　while　minimizing　redundant　computations.　Robust　theoretical　proofs　and　extensive　experimental　validations　show　that　VRT∗　efficiently　focuses　computational　resources　on　critical　workspace　in　MRS　planning,　rapidly　capturing　high-quality　initial　solutions　and　reliably　converging　to　optimal　path.　©　2025　IEEE.