Rats　possess　exceptional　navigational　abilities,　allowing　them　to　adaptively　adjust　their　navigation　paths　based　on　the　environmental　structure.　This　remarkable　ability　is　attributed　to　the　interactions　and　regulatory　mechanisms　among　various　spatial　cells　within　the　rat＇s　brain.　Based　on　these,　this　paper　proposes　a　navigation　path　search　and　optimization　method　for　mobile　robots　based　on　the　rat　brain＇s　cognitive　mechanism.　The　aim　is　to　enhance　the　navigation　efficiency　of　mobile　robots.　The　mechanism　of　this　method　is　based　on　developing　a　navigation　habit.　Firstly,　the　robot　explores　the　environment　to　search　for　the　navigation　goal.　Then,　with　the　assistance　of　boundary　vector　cells,　the　greedy　strategy　is　used　to　guide　the　robot　in　generating　a　locally　optimal　path.　Once　the　navigation　path　is　generated,　a　dynamic　self-organizing　model　based　on　the　hippocampal　CA1　place　cells　is　constructed　to　further　optimize　the　navigation　path.　To　validate　the　effectiveness　of　the　method,　this　paper　designs　several　2D　simulation　experiments　and　3D　robot　simulation　experiments,　and　compares　the　proposed　method　with　various　algorithms.　The　experimental　results　demonstrate　that　the　proposed　method　not　only　surpasses　other　algorithms　in　terms　of　path　planning　efficiency　but　also　yields　the　shortest　navigation　path.　Moreover,　the　method　exhibits　good　adaptability　to　dynamic　navigation　tasks.