We develop algorithms for estimation and control that allow a team of robots equipped with range sensors to localize an unknown target in a known but complex environment. We present an experimental model for radio-based time-of-flight range sensors. Adopting a Bayesian approach for estimation, we then develop a control law which maximizes the mutual information between the robot’s measurements and their current belief of the target position. We describe experimental results for a robot team localizing a stationary target in several representative indoor environments in which the unknown target is reliably localized with an error well below the typical error for individual measurements.
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