Dynamic task assignment for multi-UAV system in pursuit-evasion game with evolving performance evaluation model

Execution uncertainties, such as motion delay or confrontation in pursuit-evasion problems with rapidly changing states, affect the task performance of multi-Unmanned Aerial Vehicle (UAV) systems. This may lead to the failure of the initial task assignment scheme. To address this problem, this paper takes the interception scenario as a typical case. It proposes a distributed dynamic task assignment algorithm based on an evolving task performance model to reassign UAVs to tasks in an event-triggered manner. This paper combines the underlying execution model with the interception effectiveness model to design the evolving task performance model. This model describes the UAV task performance in a finite interval by predicting and integrating the states and actions of the intercepted UAVs and the targets under execution uncertainty. The discrete task monitor triggers reassignment based on the severity of the task performance deviation. The Consensus-based Auction Algorithm (CBAA) is extended to optimize the task performance function and efficiently give the reassignment scheme. Simulation results demonstrate the feasibility and effectiveness of the proposed algorithm.