TSPDiffuser: Diffusion Models as Learned Samplers for Traveling Salesperson Path Planning Problems

TL;DR

TSPDiffuser introduces a diffusion model-based data-driven approach for efficiently solving obstacle-rich traveling salesperson path planning problems by generating plausible paths and estimating travel costs.

Contribution

It presents a novel diffusion model trained on TSPPP instances to serve as a learned sampler for constructing solution roadmaps, improving efficiency and accuracy.

Findings

Outperforms existing methods in solution quality and computational efficiency.

Effective in diverse synthetic and real-world environments.

Reduces computational complexity in obstacle-rich TSP problems.

Abstract

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 travel 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.