Analysis of Solution Space for Heterogeneous Multi-UAV Cooperative Task Planning in SEAD Scenario

Suppression of Enemy Air Defenses (SEAD) is a typical scenario for the coordinated application of multi-UAV system, which has the characteristics of diverse task composition and complex temporal coordination. Task planning is a crucial process that establishes the assignment relationship between tasks and UAVs, providing a timetable and flight path for task execution. It plays a vital role in improving the coordination efficiency of UAVs. Task planning is a complex optimization problem that combines discrete variables (task assignment), continuous variables (timetable), and integer variables (UAV configuration), and is associated with multiple types of constraints. This paper comprehensively considers task arrangement, UAV assignment, and takeoff time to derive the solution space expression and analyzes the impact of factors such as targets, tasks, UAVs, and heading angles on the size of the solution space. Based on the expression of the solution space, this paper further analyzes the impact of task temporal coupling constraints, heterogeneous capability constraints. For temporal coupling constraints, the paper compares the differences in the solution space under different task combinations, analyzes the "deadlock" problem caused by the conflict between temporal coupling constraints and task execution order, and designs a rapid adjustment method suitable for multiple task combinations based on task correlation. For heterogeneous capability constraints, the paper proposes an index to describe the degree of heterogeneity of the multi-UAV system, derives the expression of the proportion of infeasible solutions caused by heterogeneous capability, and provides targeted references for UAV configuration calculation. In the simulation, this paper presents the impact of constraints on the proportion of feasible solutions under different task types, UAV, and task quantity settings, and proves that the method proposed in this paper can obtain task solutions that meet the constraints in various situations. The quantitative analysis of the solution space in this paper provides scientific guidance for task planning simulation, and the proposed processing strategies for specific constraints can effectively reduce the proportion of infeasible solutions and improve the efficiency of solving task planning problems.