论文标题
Xtenth-car：用于连接自治和全地形研究的按比例扩展的实验车辆平台
XTENTH-CAR: A Proportionally Scaled Experimental Vehicle Platform for Connected Autonomy and All-Terrain Research
论文作者
论文摘要
连接的自动驾驶汽车（CAVS）是智能运输系统（ITS）的关键组成部分，全地形自动企业地面车辆（AGV）是针对广泛应用的必不可少的工具，例如灾难响应，自动化采矿，农业，农业，军事行动，搜索，搜查和救援任务和行星探索。实验验证是CAV和AGV研究的必要条件，但使用耗时且昂贵的全尺寸车辆时需要大型，安全的实验环境。为了应对这些挑战，我们开发了Xtenth-car（用于连接的自主性和全地形研究的实验性一尺度规模的车辆平台），这是一种开源的，具有成本效益的成本成本的十分之一，由相同物理学与全尺寸的班路车辆相同的物理学管理。 Xtenth-car配备了模块（SOM），立体声摄像头，2D激光镜头和开源电子速度控制器（ESC）的一流的NVIDIA JETSON AGX ORIN系统，并配备了为机器人操作系统的两个版本编写的驱动程序（ROS 1＆ROS 2）编写的驱动程序（ROS 1＆ROS 2），以促进实验性的CAV和AGV感知，以及稳定的cav，cove and cove and Incultiment cov，运动和控制，并结合了发展，并结合了行动，并融合了型号，并结构加强学习（DRL）。 Xtenth-car设计用于紧凑的实验环境，旨在提高前期成本低的实验CAV和AGV研究的可访问性，并完整的自动驾驶汽车（AV）硬件和软件体系结构类似于与全尺寸的X-CAR实验车辆平台相似，从而实现了小规模和全尺寸车辆之间有效的跨平面开发。
Connected Autonomous Vehicles (CAVs) are key components of the Intelligent Transportation System (ITS), and all-terrain Autonomous Ground Vehicles (AGVs) are indispensable tools for a wide range of applications such as disaster response, automated mining, agriculture, military operations, search and rescue missions, and planetary exploration. Experimental validation is a requisite for CAV and AGV research, but requires a large, safe experimental environment when using full-size vehicles which is time-consuming and expensive. To address these challenges, we developed XTENTH-CAR (eXperimental one-TENTH scaled vehicle platform for Connected autonomy and All-terrain Research), an open-source, cost-effective proportionally one-tenth scaled experimental vehicle platform governed by the same physics as a full-size on-road vehicle. XTENTH-CAR is equipped with the best-in-class NVIDIA Jetson AGX Orin System on Module (SOM), stereo camera, 2D LiDAR and open-source Electronic Speed Controller (ESC) with drivers written for both versions of the Robot Operating System (ROS 1 & ROS 2) to facilitate experimental CAV and AGV perception, motion planning and control research, that incorporate state-of-the-art computationally expensive algorithms such as Deep Reinforcement Learning (DRL). XTENTH-CAR is designed for compact experimental environments, and aims to increase the accessibility of experimental CAV and AGV research with low upfront costs, and complete Autonomous Vehicle (AV) hardware and software architectures similar to the full-sized X-CAR experimental vehicle platform, enabling efficient cross-platform development between small-scale and full-scale vehicles.
