Database guided realistic grasping posture generation using inverse kinematics

This paper addresses the important issue of estimating realistic grasping p ostures, and presents a methodology and algorithm to automate the generatio n of hand and body postures during the grasp of arbitrary shaped objects. P redefined body postures stored in a database are generalized to adapt to a specific grasp using inverse kinematics. The reachable space is represented discretely dividing into small subvolumes, which enables to construct the database. The paper also addresses some common problems of articulated figu re animation. A new approach for body positioning With kinematic constraint s on both hands is described. An efficient and accurate manipulation of joi nt constraints is presented. Obtained results are quite satisfactory, and s ome of them are shown in the paper. The proposed algorithms can find applic ation in the motion of virtual actors, all kinds of animation systems inclu ding human motion, robotics and some other fields such as medicine, for ins tance, to move the artificial limbs of handicapped people in a natural way.