Vision-motion planning with uncertainty

The authors describe a framework for planning of vision and motion for a mobile robot. For planning in a real world, the uncertainty and the cost of visual recognition are important issues. A robot has to consider a tradeoff between the cost of visual recognition and the effect of information obtained by recognition. A problem is to generate a sequence of vision and motion operations based on sensor information which is an integration of the current information and the predicted next sensor data. The problem is solved by recursive prediction of sensor information and the recursive search of operations. As an example of sensor modeling, a model of stereo vision is described in which correspondence of wrong pairs of features as well as quantization error are considered. Using the framework, a robot can successfully generate a plan for real-world problem