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.

翻译：以学习为基础的方法在规划方面越来越突出,但是,在图表上很少采用学习辅助的有限路径规划方法,尽管有多种下游实际应用,例如,通常用于救灾或搜索和救援应用的自动无人驾驶地面车辆(AUGV)的有限路径规划方法;在非公路环境中,AUGV必须在各种业务制约下动态地优化源源预测路径,其中若干方法难以预先预测,需要在线处理。我们提议建立一个混合解决计划器,将机器学习模型和最佳求解器结合起来。更具体地说,一个图形共变网络(GCN)用于协助一个分支和捆绑(B&B)算法处理这些制约因素。我们在现实的情景上进行实验,并表明GCN的支持能够大大加快速度,并更顺利地推广到更困难的问题。