In the past several years, grasp analysis of multi-fingered robotic hands has been actively studied through the use of posture synergies. In these grasping planning algorithms, a formulated optimization is usually performed in the hand’s low-dimensional representation together with the hand’s position and orientation. The optimization terminates at a stable grasp, often after repeated trials with different initial guesses. Furthermore, there is no guarantee that the generated grasp leads to a smooth reach-to-grasp trajectory since the grasping planning process mostly concerns hand poses with the fingers proximal to the object. A unified theoretical framework of a gradient-based iterative algorithm is hence proposed in this paper to plan a reach-to-grasp task, predicting the grasp quality and adjusting the hand’s posture synergies, position and orientation during the approaching phase to achieve a stable grasp. The grasp quality measurement is adopted from a highly efficient pseudo-distance formulation. Stable power grasp and precision pinch can be consistently and intentionally planned with different contact conditions specified in the formulation, which means that an intention for planning a power grasp would not generate a pinch result. Several numerical simulation case studies are presented to demonstrate the effectiveness of the proposed algorithm.
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