DART: 自動運転研究のためのコンパクトプラットフォーム
DART: A Compact Platform For Autonomous Driving Research
小型の車型ロボットにセンサを追加し、機体モデル同定や運動計画の実験ができる低コストで再現可能な自動運転研究用プラットフォームを構築した。
著者: Lorenzo Lyons, Thijs Niesten, Laura Ferranti
分類: cs.RO
原文アブストラクト
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.
