Algorithms　such　as　RRT　(Rapidly　exploring　random　tree),　A*　and　their　variants　have　been　widely　used　in　the　field　of　robot　path　planning.　A　lot　of　work　has　shown　that　these　detectors　are　unable　to　carry　out　effective　and　stable　results　for　moving　objects　in　high-dimensional　space,　which　generate　a　large　number　of　multi-dimensional　corner　points.　Although　some　filtering　mechanisms　(such　as　splines　and　valuation　functions)　reduce　the　calculation　scale,　the　chance　of　collision　is　increased,　which　is　fatal　to　robots.　In　order　to　generate　fewer　but　more　effective　and　stable　feature　points,　we　propose　a　novel　multi-scale　positioning　method　to　plan　the　motion　of　the　high-dimensional　target.　First,　a　multi-scale　feature　extraction　and　refinement　scheme　for　waypoint　navigation　and　positioning　is　proposed　to　find　the　corner　points　that　are　more　important　to　the　planning,　and　gradually　eliminate　the　unnecessary　redundant　points.　Then,　in　order　to　obtain　a　stable　planning　effect,　we　balance　the　gradient　of　corner　point　classification　detection　to　avoid　over-optimizing　some　of　them　during　the　training　phase.　In　addition,　considering　the　maintenance　cost　of　the　robot　in　actual　operation,　we　pay　attention　to　the　mechanism　of　anti-collision　in　the　model　design.　Our　approach　can　achieve　a　complete　obstacle　avoidance　rate　for　high-dimensional　space　simulation　and　physical　manipulators,　and　also　work　well　in　low-dimensional　space　for　path　planning.　The　experimental　results　demonstrate　the　superiority　of　our　approach　through　a　comparison　with　state-of-the-art　models.