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This paper combines grasp analysis and manipulation planning techniques to perform fast grasp planning in complex scenes. In much previous work on grasping, the object being grasped is assumed to be the only object in the environment. Hence the grasp quality metrics and grasping strategies developed do not perform well when the object is close to obstacles and many good grasps are infeasible. We introduce a framework for finding valid grasps in cluttered environments that combines a grasp quality metric for the object with information about the local environment around the object and information about the robot's kinematics. We encode these factors in a grasp-scoring function which we use to rank a precomputed set of grasps in terms of their appropriateness for a given scene. We show that this ranking is essential for efficient grasp selection and present experiments in simulation and on the HRP2 robot.