Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Solving of Constrained Path-Planning Problems with Graph
  Convolutional Networks and Optimized Tree Search   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.

11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
7
The categories of this paper are: hep-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an