Formula Student Driverless競技を制したソフトウェアスタック
The Software Stack That Won the Formula Student Driverless Competition
360度LiDARとカメラでコーンを認識し、GraphSLAMで高精度マップを構築、最適軌道生成とMPC制御により自動運転レースカーを実現した。
著者: Andres Alvarez, Nico Denner, Zhe Feng, David Fischer, Yang Gao, Lukas Harsch, Sebastian Herz, Nick Le Large, Bach Nguyen, Carlos Rosero, Simon Schaefer, Alexander Terletskiy, Luca Wahl, Shaoxiang Wang, Jonona Yakupova, Haocen Yu
分類: cs.RO
原文アブストラクト
This report describes our approach to design and evaluate a software stack for a race car capable of achieving competitive driving performance in the different disciplines of the Formula Student Driverless. By using a 360{\deg} LiDAR and optionally three cameras, we reliably recognize the plastic cones that mark the track boundaries at distances of around 35 m, enabling us to drive at the physical limits of the car. Using a GraphSLAM algorithm, we are able to map these cones with a root-mean-square error of less than 15 cm while driving at speeds of over 70 kph on a narrow track. The high-precision map is used in the trajectory planning to detect the lane boundaries using Delaunay triangulation and a parametric cubic spline. We calculate an optimized trajectory using a minimum curvature approach together with a GGS-diagram that takes the aerodynamics at different velocities into account. To track the target path with accelerations of up to 1.6 g, the control system is split into a PI controller for longitudinal control and model predictive controller for lateral control. Additionally, a low-level optimal control allocation is used. The software is realized in ROS C++ and tested in a custom simulation, as well as on the actual race track.
