Repeatable & Scalable Multi-Vehicle Simulation with Offloaded Dynamics using Federated Modeling

R. Bhadani, J. Sprinkle
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Abstract

In this paper, we present a method to perform multi-vehicle simulation of autonomous systems that improves the repeatability of robotics simulations and can improve the scale of such simulations for dynamically complex devices such as autonomous vehicles (AV). Current approaches to simulation of multi-component AV typically infer the kinematics or dynamics through the rigid-body motion that uses joint angles and shapes. Such simulations encounter challenges for simulated AV, as the methods to discretize the behavior are prone to error accumulated over time and are computationally intensive – frequently resulting in chaotic behavior. The accumulated error results in a lack of repeatability of the simulation results. Further, when simulating multiple AVs, simulations typically fail to scale to tens of vehicles, even when slowed to permit more accurate results as the state evolves. This paper provides an architecture for improving the repeatability of simulations using federated modeling and state synchronization through a director. The method consists of replacing the inverse kinematic vehicle models with computational models of their dynamics, offloading dynamics from the physics engine for state evolution, synchronizing vehicle updates using a director, and performing the simulation at slower than real-time if needed. Our method reduces the error of trajectory deviation during repeated simulations by at least threefold. An implementation of the results of the work is presented through a Robot Operating System (ROS) package.
可重复和可扩展的多车辆仿真与卸载动力学使用联邦建模
在本文中,我们提出了一种对自主系统进行多车仿真的方法,该方法可以提高机器人仿真的可重复性,并可以提高自动驾驶汽车(AV)等动态复杂设备的仿真规模。目前的多部件自动驾驶仿真方法通常通过使用关节角度和形状的刚体运动来推断运动学或动力学。这种模拟对模拟的自动驾驶汽车来说是一个挑战,因为离散行为的方法容易随着时间的推移而累积误差,而且计算量很大,经常导致混乱行为。累积误差导致仿真结果缺乏可重复性。此外,当模拟多辆自动驾驶汽车时,模拟通常无法扩展到数十辆汽车,即使随着状态的发展,为了获得更准确的结果,也会放慢速度。本文提供了一种体系结构,用于使用联邦建模和通过指示器进行状态同步来提高仿真的可重复性。该方法包括用车辆动力学的计算模型代替车辆的逆运动学模型,从物理引擎中卸载动力学以进行状态演化,使用指示器同步车辆更新,并在需要时以低于实时的速度执行仿真。该方法将重复仿真时的轨迹偏差误差降低了至少三倍。工作结果的实现是通过机器人操作系统(ROS)包呈现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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