This paper presents an algorithm for autonomously calculating active sensing strategies applied to range sensing. The receding-horizon algorithm we use, called Ergodic Exploration of Distributed Information (EEDI), involves two major components: a) calculation of an expected information density map over the search space based on prior information and a model of the sensor, and b) ergodic trajectory optimization over the sensor configuration space with respect to that information map. The ergodic control algorithm does not rely on discretization of the search or action spaces, and is well-posed for coverage with respect to the expected information density whether the information is diffuse or localized. We simulate successful localization and discrimination of targets in a two-dimensional workspace using a fixed-location range sensor under various noise levels, and compare performance to an information maximizing strategy.
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