BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929352329920512 |
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| author | Endo, Masafumi Honda, Kohei Ishigami, Genya |
| author_facet | Endo, Masafumi Honda, Kohei Ishigami, Genya |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13318 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability Endo, Masafumi Honda, Kohei Ishigami, Genya Robotics 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 |
| title | BenchNav: Simulation Platform for Benchmarking Off-road Navigation Algorithms with Probabilistic Traversability |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.13318 |