This paper analyzes task assignment for heterogeneous air vehicles using a guaranteed conflict-free assignment algorithm, the Consensus Based Bundle Algorithm (CBBA). We extend this recently proposed algorithm to handle two realistic multi-UAV operational complications. Our first extension accounts for obstacle regions in order to generate collision free paths for UAVs. Our second extension reduces task planner sensitivity to sensor measurement noise, and thereby minimizes churning behavior in flight paths. After integrating our enhanced CBBA module with a 3D visualization and interaction software tool, we simulate multiple aircraft servicing stationary and moving ground targets. Preliminary simulation results establish that consistent, conflict-free multi-UAV path assignments can be calculated on the order of a few seconds. The enhanced CBBA consequently demonstrates significant potential for real-time performance in stressing environments.

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This paper introduces a new framework for UAV search operations and proposes a new approach to calculate the minimum number of looks needed to achieve a given level of confidence of target existence in an uncertain gridded environment. Typical search theory formulations describe the uncertainty in the environment in a probabilistic fashion, by assigning probabilities of target existence to the individual cells of the grid. While assumed to be precisely known in the search theory literature, these probabilities are often the result of prior information and intelligence, and will likely be poorly known. The approach taken in this paper models this imprecise knowledge of the prior probabilities in the individual cells using the Beta distribution and generates search actions that are robust to the uncertainty. Use of the Beta distribution leads to an analytical prediction of the number of looks in a particular cell that would be needed to achieve a specified threshold in the confidence of target existence. The analytical results are demonstrated in both an expected value setting and a framework that takes into account the variance of the posterior distribution. The effectiveness of the proposed framework is demonstrated in several numerical simulations.

U nmanned aerial vehicles (UAVs) are acquiring an increased level of autonomy as more complex mission scenarios are envisioned [1]. For example, UAVs are being used for intelligence, surveillance, and reconnaissance missions as well as to assist humans in the detection and localization of wildfires [2], tracking of moving vehicles along roads [3], [4], and performing border patrol missions [5]. A critical component for networks of autonomous vehicles is the ability to detect and localize targets of interest in a dynamic and unknown environment. The success of these missions hinges on the ability of the algorithms to appropriately handle the uncertainty in the information of the dynamic environment and the ability to cope with the potentially large amounts of communicated data that will need to be broadcast to synchronize information across networks of vehicles. Because of their relative simplicity, centralized mission management algorithms have previously been developed to create a conflict-free task assignment (TA) across all vehicles. However, these algorithms are often slow to react to changes in the fleet and environment and require high bandwidth communication to ensure a consistent situational awareness (SA) from distributed sensors and also to transmit detailed plans back to those sensors. More recently, decentralized decision-making algorithms have been proposed [6]-[8] that reduce the amount of communication required between agents and improve the robustness and reactive ability of the overall system to bandwidth limitations and fleet, mission, and environmental variations. These methods focus on individual agents generating and maintaining their own SA and TA, relying on periodic intervehicle