Planning for Autonomous Unmanned Ground Vehicles (AUGV) is still a challenge, especially in difficult, off-road, critical situations. Automatic planning can be used to reach mission objectives, to perform navigation or maneuvers. Most of the time, the problem consists in finding a path from a source to a destination, while satisfying some operational constraints. In a graph without negative cycles, the computation of the single-pair shortest path from a start node to an end node is solved in polynomial time. Additional constraints on the solution path can however make the problem harder to solve. This becomes the case when we need the path to pass through a few mandatory nodes without requiring a specific order of visit. The complexity grows exponentially with the number of mandatory nodes to visit. In this paper, we focus on shortest path search with mandatory nodes on a given connected graph. We propose a hybrid model that combines a constraint-based solver and a graph convolutional neural network to improve search performance. Promising results are obtained on realistic scenarios.

中文翻译：

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使用图卷积神经网络进行约束最短路径搜索

自主无人地面车辆 (AUGV) 的规划仍然是一个挑战，尤其是在困难、越野、危急情况下。自动规划可用于实现任务目标、执行导航或机动。大多数时候，问题在于找到从源到目的地的路径，同时满足一些操作约束。在没有负循环的图中，从开始节点到结束节点的单对最短路径的计算在多项式时间内求解。然而，解决方案路径上的额外约束会使问题更难解决。当我们需要路径通过几个强制性节点而不需要特定的访问顺序时，就会出现这种情况。复杂性随着要访问的强制性节点的数量呈指数增长。在本文中，我们专注于在给定的连通图上使用强制节点进行最短路径搜索。我们提出了一种混合模型，它结合了基于约束的求解器和图卷积神经网络，以提高搜索性能。在现实场景中获得了有希望的结果。