Due　to　the　complete　randomness　of　sampling,　the　traditional　PRM　algorithm　was　often　difficult　to　be　applied　to　the　robot　path　planning　in　the　working　environment,　including　narrow　channels.　To　this　end,　an　improved　probabilistic　roadmap　method　(Improved　PRM)　integrating　global　goal-oriented　sampling　and　local　node　enhancement　was　proposed　and　utilized　to　the　path　planning　of　a　planar　grid　map　scene　and　a　6-DOF　robot.　Firstly,　the　global　goal-directed　sampling　was　combined　with　the　random　sampling　in　the　proposed　Improved　PRM,　and　the　probability　of　global　sampling　points　falling　into　narrow　channels　was　raised　by　the　mixed　sampling,　so　as　to　achieve　the　heuristic　map　enhancement.　Secondly,　nodes　in　narrow　channels　were　extracted　by　using　the　node　weight　idea,　and　a　local　node　enhancement　strategy　based　on　Gaussian　distribution　was　used　to　expand　new　nodes　in　narrow　channels　to　enhance　the　connectivity　of　the　map　and　the　success　rate　of　path　planning.　Finally,　the　redundant　node　elimination　strategy　was　presented　to　optimize　the　initial　path　planned　by　the　algorithm.　The　simulation　results　of　the　Improved　PRM　algorithm　in　the　planar　grid　map　showed　that　the　success　rate　of　the　algorithm　for　robot　path　planning　was　more　than　89.3%.　Besides,　the　comprehensive　evaluation　and　path　quality　evaluation　were　both　higher　than　that　of　other　algorithms.　In　the　simulation　experiment　of　a　6-DOF　robot,　the　average　path　cost　obtained　by　the　Improved　PRM　algorithm　was　about　42.7%　lower　than　that　of　the　traditional　PRM　algorithm.　Meanwhile,　the　probability　of　successfully　passing　through　the　narrow　channel　was　also　68　percentage　points　higher　than　that　of　the　traditional　PRM　algorithm.　Therefore,　compared　with　other　algorithms,　the　Improved　PRM　algorithm　had　advantages　in　improving　the　success　rate　of　path　planning,　reducing　path　nodes,　and　ensuring　path　quality　in　the　working　environment　with　narrow　channels.　©　2023　Chinese　Society　of　Agricultural　Machinery.　All　rights　reserved.