Performance Characterization of a Point-Cloud-Based Path Planner in Off-Road Terrain
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Ray Launching-Based Computation of Exact Paths with Noisy Dense Point Clouds
Point clouds have been a recent interest for ray tracing-based radio channel characterization, as sensors such as RGB-D cameras and laser scanners can be utilized to generate an accurate virtual copy of a physical enviro…
Traversability Aware Autonomous Navigation for Multi-Modal Mobility Morphobot (M4)
Autonomous navigation in unstructured environments requires robots to assess terrain difficulty in real-time and plan paths that balance efficiency with safety. This thesis presents a traversability-aware navigation fram…
Point CloudsMaskPlanner: Learning-Based Object-Centric Motion Generation from 3D Point Clouds
Object-Centric Motion Generation (OCMG) plays a key role in a variety of industrial applications$\unicode{x2014}$such as robotic spray painting and welding$\unicode{x2014}$requiring efficient, scalable, and generalizable…
Motion GenerationConv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments
Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first re…
Self-Supervised LearningMotion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
This paper describes Motion Planning Networks (MPNet), a computationally efficient, learning-based neural planner for solving motion planning problems. MPNet uses neural networks to learn general near-optimal heuristics …
Continual LearningMotion Planning