Tarabanis Konstantinos author Columbia University. Computer Science Tsai Roger Y. author Allen Peter K. author Columbia University. Computer Science Columbia University. Computer Science originator text Articles 1995 English The MVP (machine vision planner) model-based sensor planning system for robotic vision is presented. MVP automatically synthesizes desirable camera views of a scene based on geometric models of the environment, optical models of the vision sensors, and models of the task to be achieved. The generic task of feature detectability has been chosen since it is applicable to many robot-controlled vision systems. For such a task, features of interest in the environment are required to simultaneously be visible, inside the field of view, in focus, and magnified as required. In this paper, we present a technique that poses the vision sensor planning problem in an optimization setting and determines viewpoints that satisfy all previous requirements simultaneously and with a margin. In addition, we present experimental results of this technique when applied to a robotic vision system that consists of a camera mounted on a robot manipulator in a hand-eye configuration. Robotics 11 1 72 85 1995-02 http://dx.doi.org/10.1109/70.345939 http://hdl.handle.net/10022/AC:P:15075 NNC NNC 2012-10-25 12:14:16 -0400 2012-10-25 12:18:46 -0400 9073 eng