In this thesis, a control paradigm for vision system pose planning is proposed for achieving reliable object motion estimates. The parameters of the vision system are controlled in the motion estimation process to adapt to the dynamic object motion behavior. A Kalman filter is employed as the motion estimator. The motion estimation uncertainties are developed as a function of the vision system parameters. The task of controlling the vision system is formulated as an optimization problem of finding the vision system poses that minimize the motion estimation uncertainties. A novel coordinate system transformation is developed to facilitate the optimization process. As a result of the transformation, the vision system poses are expressed in terms of the image plane poses. Therefore, the inverse kinematics of the vision system is needed for determining the vision system poses from the image plane poses. A novel approach for deriving closed-form solutions of this inverse kinematics problem is presented. The problem of object occlusion is formulated as an optimization constraint. A hybrid technique involving a genetic algorithm, simulated annealing and gradient-based search is proposed to search for optimal image plane poses.