This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle (UCAV) performing an air-to-surface multi-target attack (A/SMTA) mission using satellite-guided bombs.First,this problem is formulated as a variant of the traveling salesman problem (TSP),called the dynamic-constrained TSP with neighborhoods (DCT SPN).Then,a hierarchical hybrid approach,which partitions the planning algorithm into a roadmap planning layer and an optimal control layer,is proposed to solve the DCTSPN.In the roadmap planning layer,a novel algorithm based on an updatable probabilistic roadmap (PRM) is presented,which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph.In the optimal control layer,a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed,which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases.First,in the offline preprocessing phase,the algorithm constructs a PRM,and then converts the original problem into a standard asymmetric TSP (ATSP).Second,in the online querying phase,the costs of directed edges in PRM are updated first,and a fast heuristic searching algorithm is then used to solve the ATSP.Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for onlinepurposes.