Most of the routing algorithms for unmanned vehicles, that arise in data gathering and monitoring applications in the literature, rely on the Global Positioning System (GPS) information for localization. However, disruption of GPS signals either intentionally or unintentionally could potentially render these algorithms not applicable. In this article, we present a novel method to address this difficulty by combining methods from cooperative localization and routing. In particular, the article formulates a fundamental combinatorial optimization problem to plan routes for an unmanned vehicle in a GPSrestricted environment while enabling localization for the vehicle. We also develop algorithms to compute optimal paths for the vehicle using the proposed formulation. Extensive simulation results are also presented to corroborate the effectiveness and performance of the proposed formulation and algorithms.

Path planning algorithms for unmanned aerial or ground vehicles, in many surveillance applications, rely on Global Positioning System (GPS) information for localization. However, disruption of GPS signals, by intention or otherwise, can render these plans and algorithms ineffective. This article provides a way of addressing this issue by utilizing stationary landmarks to aid localization in such GPS-disrupted or GPSdenied environment. In particular, given the vehicle's path, we formulate a landmark-placement problem and present algorithms to place the minimum number of landmarks while satisfying the localization, sensing, and collision-avoidance constraints. The performance of such a placement is also evaluated via extensive simulations on ground robots.

Path planning algorithms for unmanned aerial or ground vehicles rely on Global Positioning System (GPS) information for localization in many surveillance and reconnaissance applications. However, disruption of GPS signals, by intention or otherwise, can render these algorithms ineffective. This paper provides a way of addressing this issue by leveraging range information from additionally placed stationary objects in the environment called Landmarks (LMs). The placement of LMs and the route followed by the vehicle is posed as an integer program such that the total travel and LM placement cost is minimized. The proposed formulation of the optimization problem also allows for a limited field-of-view of the sensor on-board the vehicle. For instances that are hard to solve for optimal solutions using the integer program, we present two fast heuristics to find good feasible solutions. We provide a systematic framework and algorithms for the problem, and evaluate the system using numerical, simulation and experimental results.

In this paper, we solve a discrete-time bearing-only cooperative localization problem for a team of autonomous vehicles with a special focus on switching sensing topology. A centralized Extended Kalman Filter (EKF) is used for jointly estimating position and heading of all the vehicles using local motion and relative bearing measurements. We derive discrete-time conditions for switching relative sensing topology. Furthermore, we use cooperative synchronization for generating vehicle paths that satisfy the observability conditions. The conditions are verified through simulation and experimental results.

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