Multi-domain modeling of environment and ecosystem of virtual off-road scenes for simulating ground vehicle autonomy

Christopher R Hudson, Warren Wheeler, Christopher Goodin, Daniel W Carruth, Nicholas Harvel, Joshua Ferguson, J Gabriel Monroe, David P McInnis
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Abstract

Autonomous ground vehicles (AGVs) operating in off-road terrain are influenced by a variety of factors that are unique to the off-road environment, especially the presence of vegetation. Accurately simulating the performance of AGV requires the creation of off-road virtual worlds that realistically present the characteristics of vegetation to simulations of the tires, chassis, and sensor systems. In this work, we present the development and implementation of a coupled soil moisture and vegetation growth model for generating synthetic off-road terrains for use in AGV simulations. These digital scenes have high geometric fidelity for simulating sensor systems used on AGV like terrestrial lidar. The vegetation model uses stored carbohydrates to predict growth cycles and takes multiple phenomenon into account including soil moisture, weather conditions, and seasonal variations. Results of AGV simulations in synthetic terrains are presented as a demonstration of the models utility.
用于模拟地面车辆自动驾驶的虚拟越野场景环境和生态系统多域建模
在越野地形中运行的自动地面车辆(AGV)会受到各种越野环境特有因素的影响,尤其是植被的存在。要准确模拟 AGV 的性能,就必须创建越野虚拟世界,将植被的特性真实地呈现给轮胎、底盘和传感器系统的模拟。在这项工作中,我们介绍了土壤湿度和植被生长耦合模型的开发和实施情况,该模型用于生成合成越野地形,供 AGV 模拟使用。这些数字场景具有很高的几何保真度,可用于模拟 AGV 上使用的传感器系统(如地面激光雷达)。植被模型利用储存的碳水化合物来预测生长周期,并将土壤湿度、天气条件和季节变化等多种现象考虑在内。在合成地形中模拟 AGV 的结果展示了该模型的实用性。
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