  This paper introduces a framework to plan grasps with multi-fingered hands.
The framework includes a multi-dimensional iterative surface fitting (MDISF)
for grasp planning and a grasp trajectory optimization (GTO) for grasp
imagination. The MDISF algorithm searches for optimal contact regions and hand
configurations by minimizing the collision and surface fitting error, and the
GTO algorithm generates optimal finger trajectories to reach the highly ranked
grasp configurations and avoid collision with the environment. The proposed
grasp planning and imagination framework considers the collision avoidance and
the kinematics of the hand-robot system, and is able to plan grasps and
trajectories of different categories efficiently with gradient-based methods
using the captured point cloud. The found grasps and trajectories are robust to
sensing noises and underlying uncertainties. The effectiveness of the proposed
framework is verified by both simulations and experiments.
