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一种使用ROS框架的自主道路车辆航点跟踪控制器。

A Waypoint Tracking Controller for Autonomous Road Vehicles Using ROS Framework.

作者信息

Gutiérrez Rodrigo, López-Guillén Elena, Bergasa Luis M, Barea Rafael, Pérez Óscar, Gómez-Huélamo Carlos, Arango Felipe, Del Egido Javier, López-Fernández Joaquín

机构信息

Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain.

Systems Engineering and Automation Department, University of Vigo, 36310 Vigo, Spain.

出版信息

Sensors (Basel). 2020 Jul 21;20(14):4062. doi: 10.3390/s20144062.

DOI:10.3390/s20144062
PMID:32708346
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7412167/
Abstract

Automated Driving Systems (ADSs) require robust and scalable control systems in order to achieve a safe, efficient and comfortable driving experience. Most global planners for autonomous vehicles provide as output a sequence of waypoints to be followed. This paper proposes a modular and scalable waypoint tracking controller for Robot Operating System (ROS)-based autonomous guided vehicles. The proposed controller performs a smooth interpolation of the waypoints and uses optimal control techniques to ensure robust trajectory tracking even at high speeds in urban environments (up to 50 km/h). The delays in the localization system and actuators are compensated in the control loop to stabilize the system. Forward velocity is adapted to path characteristics using a velocity profiler. The controller has been implemented as an ROS package providing scalability and exportability to the system in order to be used with a wide variety of simulators and real vehicles. We show the results of this controller using the novel and hyper realistic CARLA Simulator and carrying out a comparison with other standard and state-of-art trajectory tracking controllers.

摘要

自动驾驶系统（ADS）需要强大且可扩展的控制系统，以实现安全、高效和舒适的驾驶体验。大多数自动驾驶车辆的全局规划器输出一系列要遵循的路点。本文提出了一种用于基于机器人操作系统（ROS）的自主引导车辆的模块化且可扩展的路点跟踪控制器。所提出的控制器对路点进行平滑插值，并使用最优控制技术确保即使在城市环境中高速行驶（高达50公里/小时）时也能实现稳健的轨迹跟踪。在控制回路中对定位系统和执行器的延迟进行补偿，以稳定系统。使用速度剖析器使前进速度适应路径特征。该控制器已作为一个ROS包实现，为系统提供了可扩展性和可移植性，以便与各种模拟器和真实车辆一起使用。我们展示了使用新颖且高度逼真的CARLA模拟器的该控制器的结果，并与其他标准和先进的轨迹跟踪控制器进行了比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9b2b90b5f0e7/sensors-20-04062-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/067457671ec9/sensors-20-04062-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/e5d3697d49f0/sensors-20-04062-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/d8a4d1e2c78a/sensors-20-04062-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/c788bcc83379/sensors-20-04062-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9c258f98cc9d/sensors-20-04062-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/0521d67cee2a/sensors-20-04062-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/62d0166b982e/sensors-20-04062-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/e7e8628f80b5/sensors-20-04062-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/17a2703b2d59/sensors-20-04062-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/f5db42bfe033/sensors-20-04062-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9b2b90b5f0e7/sensors-20-04062-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/067457671ec9/sensors-20-04062-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9ee315d493b7/sensors-20-04062-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/e5d3697d49f0/sensors-20-04062-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/d8a4d1e2c78a/sensors-20-04062-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/c788bcc83379/sensors-20-04062-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9c258f98cc9d/sensors-20-04062-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/0521d67cee2a/sensors-20-04062-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/62d0166b982e/sensors-20-04062-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/e7e8628f80b5/sensors-20-04062-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/17a2703b2d59/sensors-20-04062-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/f5db42bfe033/sensors-20-04062-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3407/7412167/9b2b90b5f0e7/sensors-20-04062-g012.jpg

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The Lateral Tracking Control for the Intelligent Vehicle Based on Adaptive PID Neural Network.基于自适应PID神经网络的智能车辆横向跟踪控制
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