Cooperative UAV search strategy based on DMPC-AACO algorithm in restricted communication scenarios

Improvement of integrated battlefield situational awareness in complex environments involving dynamic factors such as restricted communications and electromagnetic interference (EMI) has become a contentious research problem. In certain mission environments, due to the impact of many interference sources on real-time communication or mission requirements such as the need to implement communication regulations, the mission stages are represented as a dynamic combination of several communication-available and communication-unavailable stages. Furthermore, the data interaction between unmanned aerial vehicles (UAVs) can only be performed in specific communication-available stages. Traditional cooperative search algorithms cannot handle such situations well. To solve this problem, this study constructed a distributed model predictive control (DMPC) architecture for a collaborative control of UAVs and used the Voronoi diagram generation method to re-plan the search areas of all UAVs in real time to avoid repetition of search areas and UAV collisions while improving the search efficiency and safety factor. An attention mechanism ant-colony optimization (AACO) algorithm is proposed for UAV search-control decision planning. The search strategy is adaptively updated by introducing an attention mechanism for regular instruction information, a priori information, and emergent information of the mission to satisfy different search expectations to the maximum extent. Simulation results show that the proposed algorithm achieves better search performance than traditional algorithms in restricted communication constraint scenarios.