Multidestination Path Planning Based on Ant Colony Decision and Rolling Control

In the context of multidestination material transportation tasks for unmanned aerial vehicles (UAVs) during wartime, task allocation and path planning play crucial roles. Although numerous research methods have been proposed, most algorithms treat these two aspects as relatively independent components, resulting in limited flexibility in decision making and real-time path planning on highly confrontational battlefields. To address this issue, this article presents a novel approach to multidestination path planning based on ant colony decision making and rolling control. First, an information sharing module is established to process real-time updated environmental data, providing a foundation for decision making and path planning. Second, a new ant colony decision method is developed by designing an objective function that minimizes consumption to determine the order of destination material transportation. In addition, an online and multiround destination decision strategy is formulated based on the updated environmental information from the information sharing module. This strategy prevents situations where total consumption for multiple destinations becomes excessive due to UAVs not considering real-time changes in material demand during transportation but making decisions only once per destination. Subsequently, the receding horizon control with extended solution method is employed to achieve multidestination path planning by integrating online path planning and decision strategies. Finally, experiments are conducted in a multidestination environment with obstacles to validate the effectiveness of our proposed method. The results demonstrate that compared to several other advanced optimization methods, our approach exhibits advantages such as shorter paths and lower overall material consumption.