Integrate Assignment of Multiple Heterogeneous Unmanned Aerial Vehicles Performing Dynamic Disaster Inspection and Validation Task With Dubins Path
Weinan Wu, Jie Xu, Yiming Sun
Abstract
The actual performance of a task-assignment method of unmanned aerial vehicles (UAVs) shows a considerable dependence on the path planning process coupled with it; however, this topic has been rarely studied in the existing literature. This article considers the problem of maximizing the task-assignment reward of a fleet of heterogeneous UAVs for a dynamic reconnaissance and confirmation task under constraints of critical time and multi-UAV tasks where the coupled path optimization objectives also need to be considered. The existing consensus-based bundle algorithm is extended with an effective method for managing the multitask and multiagent constraints. The proposed method can optimize the inherent coupling path using the Dubins path to reduce the differences between the estimated path and the actual path, shortening the operation time by adopting the distributed genetic algorithm. The proposed method is verified by the sample run tests of a disaster area reconnaissance and confirmation task and Monte Carlo comparison simulations with the two existing algorithms. The results verify both the practicality and advantages of the proposed method.