Dimensionality Reduced Pregrasp Planning for Bionic Hand Using Qdistance Quality Measurement It is a fundamental issue that the dexterous multifingered hands grasp threedimensional(3D) objects, in the field of robotic grasping and dexterous manipulation. The dexterous hands need to grasp objects fast and stable, like the hands of humans. This paper presents a strategy for the stable and fast grasp. First, the grasp progress is divided into two stages including pregrasp and grasp, which can work with a model not accurate. The algorithms of dimension reduction, Qdistance quality and simulated annealing are used together at the pregrasp to simplify the calculation and the pregrasp process. Finally, the effectiveness of the proposed methods is validated under simulations and in experiments with an dexterous multifingered hand.

optimization Model for Planning Precision Grasps with MultiFingered Hands Precision grasps with multifingered hands are important for precise placement and inhand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, highdimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multifingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of leastsquares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collisionfree optimal precision grasps efficiently. The average computation time is 0.50 secgrasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.