The NATURE Autonomy Stack - An Open-Source Stack for Off-Road Navigation
Abstract
Off-road autonomous navigation remains an ongoing challenge for autonomous ground vehicles (AGV). The challenges of navigating in an unstructured environment include identifying and detecting both positive and negative obstacles, distinguishing navigable from non-navigable vegetation, identifying soft soil, and negotiating rough or sloping terrain. While many recent works have dealt with various aspects of the off-road navigation problem, up to now there has not been a free and open-source autonomy stack for off-road that included integrated modules for perception, planning, and control. Therefore, we have recently developed the NATURE (Navigating All Terrains Using Robotic Exploration) autonomy stack as a publicly available resource to facilitate the advancement of off-road n avigation research. The NATURE stack is implemented u sing the Robotic Operating System (ROS) and can be built to work with both ROS-1 and ROS-2. The modular nature of the NATURE stack makes it an ideal resource for researchers who want to evaluate a particular algorithm for perception, planning, or control without developing an entire navigation stack from scratch. NATURE features several options for both global and local path planning including A*, artificial potential field, and spline-based planning, as well as multiple options for perception including a simple geometrically based obstacle finder and more advanced custom traversability algorithm derived from 3D lidar. In this presentation we give an overview of the NATURE stack and show some past uses of the stack in both simulated and field experiments.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 06, 2024
- Accession Number
- AD1229486
Entities
People
- Christopher Goodin
- Christopher R. Hudson
- Daniel W. Carruth
- Lucas D. Cagle
- Marc N. Moore
- Paramsothy Jayakumar
- Stefan Wapnick
Organizations
- McGill University
- Mississippi State University