Estimation of inverse kinematics of arbitrary serial chain manipulators and human-like robotic hands

The general solution for inverse kinematics is a problem known for a long time. A lot of problems, algorithms, etc., depend on inverse kinematics. While solutions for specific serial or parallel chain manipulators exist, a good estimation for an arbitrary serial robot within a short computation time is still missing. We provide not only a tool for the discrete estimation of inverse kinematics of arbitrary serial chain manipulators, but also concepts for inverse kinematics optimization along entire paths (adaptive tunneling) and an approximation of a desired grasp with a human-like robotic hand (virtual shut grasp). The latter concept includes an efficient reduction technique of a complex, arbitrary hand, which enables a fast and accurate estimation of the inverse kinematics of an entire hand. In contrast to existing work, our approach is general, since it is neither restricted to certain configurations of the serial manipulator nor to specific structures of the hand. Moreover, our approach does neither rely on proximate starting positions nor does it require specific properties of the objective function concerning the position of the minima. It works even under the presence of multiple local minima in the solution space. Experiments show the performance of our system. The results of the estimation of the inverse kinematics are very accurate and the maximally required joint speeds along the paths are low.