The　behavior-based　motion　planning　with　nonholonomic　constrains　for　lunar　rovers　is　discussed　in　this　paper.　For　each　fuzzy　behavior　controller,　the　hybrid　coordinate　scheme　which　combines　competition　with　cooperation　is　proposed　to　ensure　both　the　robustness　and　optimization.　Force　and　torque　of　the　wheels　are　chosen　as　the　output　of　fuzzy　behavior.　The　on-line　Q-learning　is　used　to　obtain　the　behavior＇s　coordinate　scheme,　ant　its　output;　is　an　optimal　solution　within　a　behavior　decision　set　which　is　obtained　according　to　the　outputs　from　each　behavior　controller.　Maggi　equations　are　introduced　to　formulate　the　rover＇s　transfer　under　the　learnt　controls.　Experiment　results　demonstrate　the　effectiveness　of　this　method　and　traceability　of　the　trajectory.