This paper presents the design of a research platform for autonomous driving applications, the Delft's Autonomous-driving Robotic Testbed (DART). Our goal was to design a small-scale car-like robot equipped with all the hardware needed for on-board navigation and control while keeping it cost-effective and easy to replicate. To develop DART, we built on an existing off-the-shelf model and augmented its sensor suite to improve its capabilities for control and motion planning tasks. We detail the hardware setup and the system identification challenges to derive the vehicle's models. Furthermore, we present some use cases where we used DART to test different motion planning applications to show the versatility of the platform. Finally, we provide a git repository with all the details to replicate DART, complete with a simulation environment and the data used for system identification.

翻译：本文介绍了面向自动驾驶应用的研究平台——代尔夫特自动驾驶机器人测试平台（DART）的设计方案。我们的目标是设计一款小型车式机器人，其配备机载导航与控制所需的全部硬件，同时兼顾成本效益与可复现性。为开发DART，我们基于现有现成模型进行构建，通过扩展其传感器套件来提升其在控制与运动规划任务中的性能。本文详述了硬件配置方案及推导车辆模型所面临的系统辨识挑战。此外，我们展示了利用DART测试不同运动规划应用的实际案例，以体现该平台的多功能性。最后，我们提供了包含复现DART所需全部细节的Git仓库，其中包含仿真环境及系统辨识所使用的原始数据。