用点云导航系统在越野地形中实现高成功率自主行驶。
Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
- 基于点云构建路径规划栈，通过参数调优提升性能。
- 仿真3万次成功率达98%，实地测试无故障。
- 初始规划阶段的扩展半径对性能影响最大，适合参数优化研究者。
我们对一种基于点云的自主越野导航系统MUONS进行了全面评估。通过模拟30,000次路径规划与导航试验，并结合实地测试验证了其性能。模拟实验涵盖三张运动学挑战性地形地图和七种路径规划参数的二十种组合。使用MUONS的自主地面车辆（AGV）在仿真中取得0.98的成功率，实地测试中未出现任何故障。通过统计与相关性分析发现，初始规划阶段采用的双向RRT（Bi-RRT）扩展半径与规划时间及路径长度最相关。此外，参数变化引起的性能波动在模拟与实地测试间表现出高度一致性，支持采用蒙特卡洛模拟进行性能评估与参数调优。
原文摘要 · Abstract (English)
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.
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