Decentralized Task Assignment for Unmanned Aerial Vehicles

This paper investigates the problem of decentralized task assignment for a fleet of UAVs. Centralized task assignment for a fleet of UAVs is often not practical due to communication limits, robustness issues, and scalability, and using a distributed approach can mitigate many of these problems. One recently proposed decentralized approach is to replicate the central assignment algorithm on each UAV. The success of this implicit coordination strongly depends on the assumption that all UAVs have the same situational awareness. Examples are presented in this paper to show that this consensus in the information is both necessary and potentially time consuming. This paper extends the basic implicit coordination approach to achieve better performance with imperfect data synchronization. The resulting robust decentralized task assignment method assumes some degree of data synchronization, but adds a second planning step based on sharing the planning data. The approach is analogous to closing a synchronization loop on the planning process to reduce the sensitivity to exogenous disturbances. Further simulations are presented to show the advantages of this method in reducing the conflicts in the assignments, resulting in improved performance compared to implicit coordination. These results also clearly demonstrate the effect of communication at the different stages of the planning algorithm on the overall mission performance.