Constrained Shortest Path Search with Graph Convolutional Neural Networks

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. That becomes the case if we require the path to pass by a few mandatory nodes without requiring a specific order of visit. The complexity grows exponentially as we require more 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 constraintbased solver and a graph convolutional neural network to improve search performance. Promising results are obtained on realistic scenarios.