Abstract
We study the multiagent unmanned aerial vehicle (UAV) routing problem where a set of UAVs needs to collect information via surveillance of an area of operation. Each UAV is autonomous and does not rely on a reliable communication medium to coordinate with other UAVs. We formulate the problem as a game where UAVs are players and their strategies are the different routes they can take. Our model also incorporates the useful concept of information fusion. This results in a new variant of weighted congestion-type games. We show that the price of anarchy (PoA) of the game is at most 2, irrespective of the number of UAVs and their sensor capabilities. This also validates the empirical results of earlier works. Furthermore, we identify classes of games for the existence of a pure Nash equilibrium. To the best of our knowledge, these are the first such theoretical results in the related literature. Finally, we conduct experimental studies using randomly generated instances with several multiagent UAV routing policies. Our insights are that PoA increases with the congestion level when the same number of UAVs search a smaller area or more UAVs search the same area, and on an average, our proposed policies are less than 10% worse than the centralized optimal for the problem scenarios attempted. Note to Practitioners —UAVs are becoming increasingly popular for information collection tasks in defense and civilian applications alike. When the collection area is large, it is not unusual that a fleet of UAVs is deployed. Routing of a fleet can be performed in a centralized or decentralized manner. Decentralized routing might be the only possibility when centralized situational awareness is not possible due to bandwidth limitations and centralized optimal routes for each UAV in the fleet are too complex to compute. Autonomous solutions have several other advantages, let alone simplicity. For managers of UAV systems, our work provides the first theoretical characterization of how bad could decentralized routing be. Under various scenarios of information fusion, specifically weak and strong, and the attribution of information collected to each UAV of a team, we prove that the fleet will collect at least 50% of the best-centralized solution. Empirically, we show that, in fact, the performance of the fleet is much better and generally not worse than 10% of the best-centralized solution. Hopefully, our routing strategies provide valuable guidance to the practicing engineer or manager of a UAV fleet.
Highlights
Unmanned aerial vehicles (UAVs) are increasingly being used for intelligence, defense, and civilian information gathering and monitoring
In case of a homogeneous fleet, i.e., when each UAV has the same ρ, we show that price of anarchy (PoA) and price of stability (PoS) are at most 2 − 1/ p and they are tight
For the single-step UAV game, the existence of Finite Improvement Property (FIP) trivially implies that the problem of computing a pure NE (PNE) is in the class polynomial local search (PLS) [44]—as the problem can be reduced to finding a sink in a directed acyclic graph formed over the outcomes with the directed edges capturing the unilateral improvement deviations
Summary
Unmanned aerial vehicles (UAVs) are increasingly being used for intelligence, defense, and civilian information gathering and monitoring. A fleet of UAVs is dispatched for large geographical coverage and multiple intelligence, surveillance, and reconnaissance (ISR) missions where the goal is to maximize the amount of information collected, and this results in the problem of routing and coordination This problem can be formulated into two different ways depending on the application and environment. In case of more than two UAVs visiting a region together, the total value function, which captures the total amount of information collected, and the individual shares are defined Note that it is independent of the order in which the UAVs are considered. They apply to a more general solution concept called (coarse) correlated equilibrium of the game
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