Consensus-Based Auction Approaches for Decentralized Task Assignment

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.