We consider the problem of gathering bearing data in order to localize targets. We start with a commonly used notion of uncertainty based on Geometric Dilution of Precision (GDOP) and study the following bi-criteria problem. Given a set of potential target locations and an uncertainty level U , compute an ordered set of measurement locations for a single robot which (i) minimizes the total cost given by the travel time plus the time spent in taking measurements, and (ii) ensures that the uncertainty in estimating the target's location is at most U regardless of the targets' locations. We present an approximation algorithm and prove that its cost is at most 28.9 times the optimal cost while guaranteeing that the uncertainty is at most 5.5U. In addition to theoretical analysis, we validate the results in simulation and experiments performed with a directional antenna used for tracking invasive fish.

No abstract

This paper considers the problem of choosing measurement locations of an aerial robot in an online manner in order to localize an animal with a radio collar. The aerial robot has a commercial, low-cost antenna and USB receiver to capture the signal. It uses its own movement to obtain a bearing measurement. The uncertainty in these measurements is assumed to be bounded and represented as wedges. The measurements are then merged by intersecting the wedges. The localization uncertainty is quantified by the area of the resulting intersection. The goal is to reduce the localization uncertainty to a value below a given threshold in minimum time. We present an online strategy to choose measurement locations during execution based on previous readings and analyze its performance with competitive analysis. We also validate the strategy in simulations and in field experiments over a 5 hectare area using an autonomous aerial robot equipped with a directional antenna.

No abstract

In this paper we study the problem of forming coalitions for dynamic tasks in multirobot systems. As robots, either individually or in groups, encounter new tasks for which individual or group resources do not suffice, robot coalitions that are collectively capable of meeting these requirements need to be formed. We propose a hybrid approach to this problem where coalitions proceed with the task if they have sufficient resources after liberating redundant members while they report it to a task coordinator in cases where their resources do not suffice. In turn, the task coordinator forms capable coalitions based on coalition formation games in which groups of robots are evaluated together in regards to each task's required resources and cost of forming a coalition. The resulting coalitions are such that no group of robots has a viable alternative to staying within their assigned coalition. Thus, as new tasks are confronted, coalitions merge and split so that the resulting coalitions are capable of the newly encountered tasks. Simulations and experiments performed on groups of heterogeneous mobile robots demonstrate the effectiveness of the proposed approach.