Multi-UAV Task Reallocation Based on Dynamic Window Consensus-Based Bundle Algorithm

Unmanned aerial vehicles (UAVs) have been extensively utilized in performing combat tasks due to their flexibility and low cost. The utilization of multi-UAVs can combine the strengths of each UAV, expand the search area, and enhance combat capabilities. However, the environment during task execution is not always consistent. The task allocation plan created before combat may become obsolete in the presence of unexpected changes and threats. To rapidly respond to these changes, this paper proposes a distributed dynamic window consensus-based bundle algorithm (DWCBBA) to address the task reallocation problem for multi-UAVs under sudden threats. An improved distributed task allocation framework is introduced, which allows multi-UAVs to share information and negotiate for consensus within dynamic cyclic time windows, ensuring timely handling of sudden threat events. The bidding strategy based on CBBA is optimized by factoring in the influence of threat path length to solve the model based on the effectiveness of reallocated tasks. Simulation results demonstrate that this approach facilitates the effective and rapid resolution of task reallocation under sudden threats.