Abstract
Forest fires pose a significant threat to human life and property,so the utilization of unmanned aircraft systems provides new ways for forest firefighting.Given the constrained load capacities of these aircraft,aerial refueling becomes crucial to extend their operational time and range.In order to address the complexities of firefighting missions involving multi-receiver and multi-tanker deployed from various airports,first,a fuel consumption calculation model for aerial refueling scheduling is established based on the receiver path.Then,two distinct methods,including an integrated one and a decomposed one,are designed to address the challenges of establishing refueling airspace and allocating tasks for tankers.Both methods aim to optimize total fuel con-sumption of the receivers and tankers within the aerial refueling scheduling framework.The opti-mization problem is established as nonlinear optimization models along with restrictions.The integrated method seamlessly combines refueling rendezvous point scheduling and tanker task allo-cation into unified process.It has a complete solution space and excels in optimizing total fuel con-sumption.The decomposed method,through the separation of rendezvous point scheduling and task allocation,achieves a reduced computational complexity.However,this comes at the cost of sacrificing optimality by excluding specific feasible solutions.Finally,numerical simulations are car-ried out to verify the feasibility and effectiveness of the proposed methods.These simulations yield insights crucial for the practical engineering application of both the integrated and decomposed methods in real-world scenarios.This comprehensive approach aims to enhance the efficiency of forest firefighting operations,mitigating the risks posed by forest fires to human life and property.
基金项目
National Natural Science Foundation of China(61833013)
National Natural Science Foundation of China(61473012)
National Natural Science Foundation of China(62103335)
Key Research Program of Jiangxi Province in China(20192BBEL50005)
维普
万方数据