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

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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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          Author and article information

          Journal
          21 May 2024
          Article
          2405.13318
          c9017e76-8a37-46a4-8eb9-0e18739a9126

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          5 pages, 2 figures. This article has been accepted for presentation at the IEEE ICRA 2024 Workshop on Resilient Off-road Autonomy
          cs.RO

          Robotics
          Robotics

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