Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search

• URL: http://arxiv.org/abs/2108.01036v1
• Date: Mon, 2 Aug 2021 16:53:21 GMT
• Title: Optimal Solving of Constrained Path-Planning Problems with Graph Convolutional Networks and Optimized Tree Search
• Authors: Kevin Osanlou, Andrei Bursuc, Christophe Guettier, Tristan Cazenave and Eric Jacopin
• Abstract summary: We propose a hybrid solving planner that combines machine learning models and an optimal solver. We conduct experiments on realistic scenarios and show that GCN support enables substantial speedup and smoother scaling to harder problems.
• Score: 12.457788665461312
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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