Learning-based methods are growing prominence for planning purposes. However, there are very few approaches for learning-assisted constrained path-planning on graphs, while there are multiple downstream practical applications. This is the case for constrained path-planning for Autonomous Unmanned Ground Vehicles (AUGV), typically deployed in disaster relief or search and rescue applications. In off-road environments, the AUGV must dynamically optimize a source-destination path under various operational constraints, out of which several are difficult to predict in advance and need to be addressed on-line. We propose a hybrid solving planner that combines machine learning models and an optimal solver. More specifically, a graph convolutional network (GCN) is used to assist a branch and bound (B&B) algorithm in handling the constraints. We conduct experiments on realistic scenarios and show that GCN support enables substantial speedup and smoother scaling to harder problems.

中文翻译：

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使用图卷积网络和优化树搜索优化求解受限路径规划问题

基于学习的方法在规划方面越来越重要。然而，在图上进行学习辅助约束路径规划的方法很少，而有多种下游实际应用。自主无人地面车辆 (AUGV) 的受限路径规划就是这种情况，通常部署在救灾或搜救应用中。在越野环境中，AUGV 必须在各种操作约束下动态优化源-目的地路径，其中几个很难提前预测，需要在线解决。我们提出了一种混合求解规划器，它结合了机器学习模型和最优求解器。更具体地说，图卷积网络 (GCN) 用于协助分支定界 (B&B) 算法处理约束。