Online Path Decision of No-Fly Zones Avoidance for Hypersonic Vehicles Based on a Graph Attention Network

The path decision problem for a hypersonic vehicle has been recently formulated as it helps to determine a good initial guess for trajectory optimization with no-fly zone constraints. This path decision problem is a hybrid problem. Discrete variables optimize paths and continuous variables optimize dynamics, which consumes costly computation time and is hard to apply in online scenarios. To reduce the computation, instead of directly solving the hybrid problem, we design a heuristics approach taking advantage of the interpretability and flexibility of a graph attention network (GAT). The path decision is modeled by a directed graph and transformed into a GAT training problem, and the resulting GAT can directly output a path in online use. During this work, there are two innovations: 1) GAT customization and 2) offline training. First, we define the mask to express the graph structure, and model the problem-specific decoder process in the GAT, thus meeting the path decision logic of no-fly zones avoidance and ensuring the solutions are feasible. Second, we numerically integrate dynamics by a path-following guidance law, calculate the total control effort as the cost function, and use this cost to train the GAT based on the widely used REINFORCE, thus conforming to dynamics that are practical for hypersonic vehicles. Simulation results illustrate the high-accuracy path decision, much faster calculation, and generalization on no-fly zone layouts and numbers.