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arXiv · 2405.13318

BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability

Masafumi Endo·Kohei Honda·Genya Ishigami
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Abstract

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

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Masafumi Endo, Kohei Honda, Genya Ishigami. 2024-05-22. BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability. https://arxiv.org/abs/2405.13318

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