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

Masafumi Endo, Kohei Honda, Genya Ishigami

TL;DR

BenchNav addresses the challenge of benchmarking off-road navigation under uncertain traversability by providing an open-source, PyTorch-based platform built on Gymnasium. It integrates a synthetic terrain data pipeline, probabilistic traversability prediction via mixtures of Gaussian processes and a terrain classifier, and unified planning execution across three solver families (A*/DWA, CL-RRT, MPPI) with CVaR risk handling. Key contributions include a reproducible data-generation scheme, built-in ML models for traversability with uncertainty, and a consistent evaluation framework across standard, geometric, and shading-challenged environments, enabling analysis of how domain shifts impact planning. The platform supports rigorous comparisons of planning methods under uncertainty and lays groundwork for future extensions to end-to-end learning and reinforcement learning in off-road navigation.

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

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

TL;DR

BenchNav addresses the challenge of benchmarking off-road navigation under uncertain traversability by providing an open-source, PyTorch-based platform built on Gymnasium. It integrates a synthetic terrain data pipeline, probabilistic traversability prediction via mixtures of Gaussian processes and a terrain classifier, and unified planning execution across three solver families (A*/DWA, CL-RRT, MPPI) with CVaR risk handling. Key contributions include a reproducible data-generation scheme, built-in ML models for traversability with uncertainty, and a consistent evaluation framework across standard, geometric, and shading-challenged environments, enabling analysis of how domain shifts impact planning. The platform supports rigorous comparisons of planning methods under uncertainty and lays groundwork for future extensions to end-to-end learning and reinforcement learning in off-road navigation.

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
Paper Structure (15 sections, 3 equations, 4 figures, 1 table)

This paper contains 15 sections, 3 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Statement
  3. BenchNav
  4. Synthetic Terrain Data Generation
  5. Probabilistic Traversability Prediction
  6. Path and Motion Planning Execution
  7. Simulation Experiments
  8. Implementation Details
  9. Search-based method
  10. Sampling-based method
  11. Optimization-based method
  12. Experimental Setups
  13. Results
  14. Limitations and Possible Extensions
  15. Conclusion

Figures (4)

  • Figure 1: BenchNav, a platform designed to solve off-road navigation problems, consists of A) synthetic terrain data generation, B) probabilistic traversability prediction based on the classify-then-regress method, and C) path and motion planning execution with given problem formulations and solvers.
  • Figure 2: Search-based: A*+DWA
  • Figure 3: Sampling-based: CL-RRT
  • Figure 4: Optimization-based: MPPI