This paper presents novel and efficient kinematic modeling techniques for ''hyper-redundant'' robots. This approach is based on a ''backbone curve'' that captures the robot's macroscopic geometric features. The inverse kinematic, or ''hyper-redundancy resolution,'' problem reduces to determining the time varying backbone curve behavior. To efficiently solve the inverse kinematics problem, we introduce a ''modal'' approach, in which a set of intrinsic backbone curve shape functions are restricted to a modal form. The singularities of the modal approach, modal non-degeneracy conditions, and modal switching are considered. For discretely segmented morphologies, we introduce ''fitting'' algorithms that determine the actuator displacements that cause the discrete manipulator to adhere to the backbone curve. These techniques are demonstrated with planar and spatial mechanism examples. They have also been implemented on a 30 degree-of-freedom robot prototype.
