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Consider a mobile robot tasked with localizing targets at unknown locationsby obtaining relative measurements. The observations can be bearing or rangemeasurements. How should the robot move so as to localize the targets andminimize the uncertainty in their locations as quickly as possible? Mostexisting approaches are either greedy in nature or rely on accurate initialestimates.  We formulate this path planning problem as an unsupervised learning problemwhere the measurements are aggregated using a Bayesian histogram filter. Therobot learns to minimize the total uncertainty of each target in the shortestamount of time using the current measurement and an aggregate representation ofthe current belief state. We analyze our method in a series of experimentswhere we show that our method outperforms a standard greedy approach. Inaddition, its performance is also comparable to an offline algorithm which hasaccess to the true location of the targets.

Active localization of multiple targets using noisy relative measurements