Path　planning　of　mobile　robots　plays　an　important　role　in　improving　work　efficiency　in　the　manufacturing　industry.　On　one　hand,　the　sampling-based　path　planning　methods　commonly　used　in　mobile　robots　require　extensive　and　time-consuming　iterative　searches　to　find　a　relatively　optimal　path　due　to　their　stochastic　nature.　On　the　other　hand,　the　map　information　obtained　during　planning　is　solely　utilized　for　collision　detection　without　being　effectively　leveraged.　This　article　proposes　a　map-informed　rapidly　random-exploring　tree　(MIRRT)　algorithm　that　combines　uniform　sampling,　rapidly　crossing　obstacles　(RCO)　and　progressive　optimization.　It　can　utilize　map　information　to　quickly　guide　the　search　tree.　MIRRT　involves　a　preprocessing　step　that　requires　segmenting　concave　obstacles　into　convex　obstacles　to　prevent　path　loss.　During　the　growth　of　the　search　tree,　the　RCO　method　guided　by　map　information　can　rapidly　discover　high-quality　ground　path　nodes　and　efficiently　navigate　obstacles　when　they　block　the　path.　Finally,　simulations　and　physical　experiments　were　performed　to　prove　that　MIRRT　significantly　outperforms　existing　state-of-the-art　sampling-based　planners　designed　specifically　for　mobile　robots.