For　the　navigation　of　Pioneer3-DX　mobile　robot　in　unknown　environment,　we　propose　a　self-navigation　strategy　with　learning　reinforcement,　and　develop　the　navigation　algorithm　based　on　the　dynamical　neural　network.　The　dynamically　self-organizing　neural　network　can　automatically　adjust　its　structure　according　to　the　complexity　of　the　working　environments　of　the　mobile　robot　to　realize　the　mapping　between　environmental　states　and　robot　actions,　effectively　avoiding　the　dimension　explosion　in　learning　reinforcement.　Simulations　and　real　robot　navigation　experiments　are　carried　out;　results　show　that　the　proposed　method　is　effective　in　applications.　It　gives　a　better　navigation　performance　than　that　of　the　artificial　potential-field　method.