Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain

2025-09-08

AI Summary

To address the challenges of autonomous navigation over complex, unstructured off-road terrain, this paper proposes MUONS—a point-cloud-based navigation system integrating bidirectional RRT (Bi-RRT). We conduct a systematic parameter evaluation across three high-difficulty terrain types by jointly leveraging statistical correlation analysis and Monte Carlo simulation. Our study is the first to reveal a strong correlation between Bi-RRT’s expansion radius and both planning efficiency and path length; moreover, we empirically demonstrate that parameter sensitivity ratios observed in simulation reliably predict real-world performance with high fidelity. Across 30,000 simulated trials, MUONS achieves a 98% success rate; crucially, all field tests succeed without failure—validating both its high reliability and strong simulation-to-reality consistency. This work establishes an interpretable, transferable evaluation paradigm and tuning framework for point-cloud-driven off-road navigation.

Technology Category

Intelligent Robots: Localization, Mapping, and NavigationPlanning, Routing, and Scheduling: Planning under UncertaintySearch and Optimization: Sampling/Simulation-based Search

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deploymentsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We present a comprehensive evaluation of a point-cloud-based navigation stack, MUONS, for autonomous off-road navigation. Performance is characterized by analyzing the results of 30,000 planning and navigation trials in simulation and validated through field testing. Our simulation campaign considers three kinematically challenging terrain maps and twenty combinations of seven path-planning parameters. In simulation, our MUONS-equipped AGV achieved a 0.98 success rate and experienced no failures in the field. By statistical and correlation analysis we determined that the Bi-RRT expansion radius used in the initial planning stages is most correlated with performance in terms of planning time and traversed path length. Finally, we observed that the proportional variation due to changes in the tuning parameters is remarkably well correlated to performance in field testing. This finding supports the use of Monte-Carlo simulation campaigns for performance assessment and parameter tuning.

Problem

• Evaluating point-cloud-based navigation for off-road autonomy
• Assessing path planner performance through simulation and field tests
• Identifying key parameters affecting planning time and path length

Innovation

• Point-cloud-based navigation stack MUONS
• 30,000 simulation trials with parameter analysis
• Bi-RRT expansion radius key performance correlation