BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability
Keywords: off-road navigation, path and motion planning, simulation platform, traversability prediction
TL;DR: We present a simulation platform for off-road navigation problems. The platform allows fair algorithm selection owing to versatile navigation simulation across different planning methods with built-in ML traversability models.
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
Submission Number: 20
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