In this work, we present an integrated approach for planning collision-free grasping motions. Therefore, rapidly exploring random tree (RRT)-based algorithms are used to build a tree of reachable and collision-free configurations. During tree generation, both grasp hypotheses and approach movements toward them are computed. The quality of reachable grasping poses is evaluated using grasp wrench space (GWS) analysis. We present an extension to a dual-arm planner that generates bimanual grasps together with collision-free dual-arm grasping motions. The algorithms are evaluated with different setups in simulation and on the humanoid robot ARMAR-III (Figure 1).