We have developed an original planner, aSyMov, that has been specially designed to address intricate robot planning problems where geometric constraints cannot be simply “abstracted” in a way that has no influence on the symbolic plan. This paper presents the ingredients that allowed us to establish an effective link between the representations used by a symbolic task planner and the represen- tations used by a realistic motion and manipulation planning library. The architecture and the main plan search strategies are presented to- gether with an illustrative example solved by a prototype implemen- tation of aSyMov. At each step of the planning process both sym- bolic and geometric constraints are considered. Besides, the plan- ning process tries to arbitrate between finding a plan with the level of knowledge it has already acquired, or “investing” more in a deeper knowledge of the topology of the different configuration spaces it manipulates.