  As robotic navigation techniques in perception and planning advance, mobile
robots increasingly venture into off-road environments involving complex
traversability. However, selecting suitable planning methods remains a
challenge due to their algorithmic diversity, as each offers unique benefits.
To aid in algorithm design, we introduce BenchNav, an open-source PyTorch-based
simulation platform for benchmarking off-road navigation with uncertain
traversability. Built upon Gymnasium, BenchNav provides three key features: 1)
a data generation pipeline for preparing synthetic natural environments, 2)
built-in machine learning models for traversability prediction, and 3)
consistent execution of path and motion planning across different algorithms.
We show BenchNav's versatility through simulation examples in off-road
environments, employing three representative planning algorithms from different
domains. https://github.com/masafumiendo/benchnav
