Physiological　studies　have　shown　that　rats　in　a　dark　environment　rely　on　the　limbs　and　vestibule　for　their　self-motion　information,　which　can　maintain　the　specific　firing　patterns　of　grid　cells　and　hippocampal　CA3　place　cells.　In　the　development　stage　of　rats,　grid　cells　are　considered　to　come　from　place　cells,　and　place　cells　can　be　encoded　by　hippocampal　theta　cells.　Based　on　these,　the　quadruped　robot　is　used　as　a　platform　in　this　paper.　Firstly,　the　sensing　information　of　the　robot＇s　limbs　and　inertial　measurement　unit　is　obtained　to　solve　its　position　in　the　environment.　Then　the　position　information　is　encoded　by　theta　cells　and　mapped　to　place　cells　through　a　neural　network.　After　obtaining　the　place　cells　with　single-peak　firing　fields,　Hebb　learning　is　used　to　adjust　the　connection　weight　of　the　neural　network　between　place　cells　and　grid　cells.　In　order　to　verify　the　model,　3-D　simulation　experiments　are　designed　in　this　paper.　The　experiment　results　show　that　with　the　robot　exploring　in　space,　the　spatial　cells　firing　effects　obtained　by　the　model　are　consistent　with　the　physiological　research　facts,　which　lay　the　foundation　for　the　bionic　environmental　cognition　model.