This paper addresses task allocation to coordinate a fleet of autonomous vehicles by presenting two decentralized algorithms: the consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, i.e., the consensus-based bundle algorithm (CBBA). These algorithms utilize a market-based decision strategy as the mechanism for decentralized task selection and use a consensus routine based on local communication as the conflict resolution mechanism to achieve agreement on the winning bid values. Under reasonable assumptions on the scoring scheme, both of the proposed algorithms are proven to guarantee convergence to a conflict-free assignment, and it is shown that the converged solutions exhibit provable worst-case performance. It is also demonstrated that CBAA and CBBA produce conflict-free feasible solutions that are robust to both inconsistencies in the situational awareness across the fleet and variations in the communication network topology. Numerical experiments confirm superior convergence properties and performance when compared with existing auction-based task-allocation algorithms.
This paper addresses task assignment in the coordination of a fleet of unmanned vehicles by presenting two decentralized algorithms: consensus-based auction algorithm (CBAA) and its generalization to the multi-assignment problem, consensus-based bundle algorithm (CBBA). These algorithms utilize a market-based decision strategy as the mechanism for decentralized task selection, and use a consensus routine based on local communication as the conflict resolution mechanism by achieving agreement on the winning bid values. The conflict resolution process of CBBA is further enhanced to address the dependency of the score value on the previously selected tasks in the multi-assignment setting. This work shows that the proposed algorithms, under reasonable assumptions on the scoring scheme and network connectivity, guarantee convergence to a conflict-free assignment. Also, the converged solutions are shown to guarantee 50% optimality in the worst-case and to exhibit provably good performance on average. Moreover, the proposed algorithms produce a feasible assignment even in the presence of inconsistency in situational awareness across the fleet, and even when the score functions varies with time in some standard manner. Numerical experiments verify quick convergence and good performance of the presented methods for both static and dynamic assignment problems.
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
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