  This paper presents TSPDiffuser, a novel data-driven path planner for
traveling salesperson path planning problems (TSPPPs) in environments rich with
obstacles. Given a set of destinations within obstacle maps, our objective is
to efficiently find the shortest possible collision-free path that visits all
the destinations. In TSPDiffuser, we train a diffusion model on a large
collection of TSPPP instances and their respective solutions to generate
plausible paths for unseen problem instances. The model can then be employed as
a learned sampler to construct a roadmap that contains potential solutions with
a small number of nodes and edges. This approach enables efficient and accurate
estimation of traveling costs between destinations, effectively addressing the
primary computational challenge in solving TSPPPs. Experimental evaluations
with diverse synthetic and real-world indoor/outdoor environments demonstrate
the effectiveness of TSPDiffuser over existing methods in terms of the
trade-off between solution quality and computational time requirements.
