OL-CBBA: An Online Task Allocation Algorithm under Weak Communication Conditions.

Multiple unmanned aerial vehicles (UAVs) and multiple tasks allocation problem is difficult to solve. Existing task allocation algorithms assume that the UAVs’ position is static, and cannot assign tasks with the changing UAVs’ position simultaneously during task execution. Those algorithms reduce the efficiency of task allocation. In this paper, we propose an online consensus-based bundle algorithm (OL-CBBA) under weak communication for dynamic task allocation. We consider the situation that the location information of UAVs will constantly change during the dynamic execution of tasks. The algorithm first improves the static CBBA to an online algorithm by updating the task marginal score in task path. Moreover, we specify a flag for the convergence of individual tasks, allowing UAVs to start executing tasks earlier. Extensive comparative experiments prove the highly consistent efficiency of OL-CBBA under weak communication conditions. Specifically, the proposed OL-CBBA attains up to 22% improvement compared with CBBA.