Abstract:

RIn the near future, more and more robots will substitute human to serve tasks, multi-fingered robot hand will be one of the robots. Since the degrees of freedom of a dexterous hand are more complex than a simple gripper, how to grasp various types of objects efficiently in a short time has become more and more important. This thesis focuses mainly on how to recognize the objects and their poses in three-dimensioned point cloud based on construction of the database. Then the grasp posture of the robot hand will be searched in the database in terms of the object pose. Finally, the rapidly-exploring random trees (RRT) – connect is used to search for the collision-free path in the joint space of the robot arm. For those objects not in our database, we can also use the simulator to find the final grasp posture according to wrench space and quality measure.