Different　from　the　past　with　the　state-action　as　the　index,　a　method　of　establishing　Q-value　table　by　discretizing　time　was　introduced.　The　problem　of　selecting　an　action　in　a　certain　state　was　transformed　into　the　problem　of　choosing　an　action　at　a　certain　time,　which　achieved　the　application　of　Q　learning　algorithm　in　dynamic　continuous　environment.　Firstly　a　genetic　algorithm　for　global　path　planning　was　adopted.　Then　the　obstacle　was　dynamically　avoided　through　Q-learning.　The　whole　system　followed　a　successive　“offline”　and　“online”　multi-layer　path　planning　philosophy.　Indicated　by　the　experiment　results,　a　path　planning　system　of　mobile　robot　is　achieved,　and　the　proposed　methods　are　state-of-the-art.　©　2017,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.