We study the problem of actively locating a static target using mobile robots equipped with bearing sensors. The goal is to reduce the uncertainty in the target's location to a value below a given threshold in minimum time. Our cost formulation explicitly models time spent in traveling, as well as taking measurements. In addition, we consider distance-based communication constraints between the robots. We provide the following theoretical results. First, we study the properties of an optimal offline strategy for one or more robots with access to the target's true location. We derive the optimal offline algorithm and bound its cost when considering a single robot or an even number of robots. In other cases, we provide a close approximation. Second, we provide a general method of converting the offline algorithm into an online adaptive algorithm (that does not have access to the target's true location), while preserving near optimality. Using these two results, we present an online strategy proven to locate the target up to a desired uncertainty level at near-optimal cost. In addition to theoretical analysis, we validate the algorithm in simulations and multiple field experiments performed using autonomous surface vehicles carrying radio antennas to localize radio tags.
- marine robotics
- networked robots
- path planning for multiple mobile robot systems