  Deep learning-based methods are growing prominence for planning purposes. In
this paper, we present a hybrid planner that combines a graph machine learning
model and an optimal solver based on branch and bound tree search for
path-planning tasks. More specifically, a graph neural network is used to
assist the branch and bound algorithm in handling constraints associated with a
desired solution path. There are multiple downstream practical applications,
such as Autonomous Unmanned Ground Vehicles (AUGV), typically deployed in
disaster relief or search and rescue operations. In off-road environments,
AUGVs 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 online. We conduct experiments on realistic
scenarios and show that graph neural network support enables substantial
speedup and smoother scaling to harder path-planning problems. Additionally,
information provided by the graph neural network enables the approach to
outperform problem-specific handcrafted heuristics, highlighting the potential
graph neural networks hold for path-planning tasks.

Artificial Intelligence;Machine Learning;Robotics