Constrained Shortest Path Search with Graph Convolutional Neural Networks

TL;DR

This paper introduces a hybrid approach combining constraint-based methods and graph convolutional neural networks to efficiently solve constrained shortest path problems with mandatory nodes in complex graphs, relevant for autonomous vehicle navigation.

Contribution

It presents a novel hybrid model that integrates constraint solvers with graph neural networks to improve shortest path search performance under complex constraints.

Findings

Promising results on realistic navigation scenarios.

Improved search efficiency over traditional methods.

Effective handling of mandatory nodes without fixed order.

Abstract

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.