Dynamic Task Assignment of Multiple Heterogeneous Autonomous Aerial Vehicles Based on Consensus With Uncertainties

This study addresses the problem of maximizing the task-assignment reward of a fleet of heterogeneous autonomous aerial vehicles (AAVs) in a dynamic reconnaissance and confirmation task in uncertain scenarios and multi-AAV tasks, where the coupled path optimization objectives need to be considered. The existing consensus-based bundle algorithm is extended using an effective method for managing multitask and multiagent constraints. In addition, the Bayesian estimation is adopted to handle uncertainties in a given scenario. The proposed method is verified by the sample run tests on a disaster area reconnaissance and confirmation task. The test results verify both the practicality and advantages of the proposed method. Finally, a robust extension to the consensus-based bundle algorithm that handles coupling with the path planning optimization in dynamic search and rescue scenarios, including tasks with multi-AAV service requirements and time-critical constraints, is introduced.