AMZFormulaStudent | Optimization-Based Hierarchical Motion Planning for Autonomous Racing @AMZFormulaStudent | Uploaded March 2020 | Updated October 2024, 1 hour ago.
In this work we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.
The presented paper can be downloaded here: arxiv.org/abs/2003.04882
_____________________________________________________
Visit our website and follow us on social media!
AMZ driverless Website: driverless.amzracing.ch
AMZ Racing on Facebook: facebook.com/amzracing
AMZ Racing on Twitter: twitter.com/amzracing?lang=en
AMZ Racing on Instagram: instagram.com/amzracing/?hl=en
AMZ Racing on LinkedIn: linkedin.com/company/akademischer-motorsportverein-z%C3%BCrich-amz-
In this work we propose a hierarchical controller for autonomous racing where the same vehicle model is used in a two level optimization framework for motion planning. The high-level controller computes a trajectory that minimizes the lap time, and the low-level nonlinear model predictive path following controller tracks the computed trajectory online. Following a computed optimal trajectory avoids online planning and enables fast computational times. The efficiency is further enhanced by the coupling of the two levels through a terminal constraint, computed in the high-level controller. Including this constraint in the real-time optimization level ensures that the prediction horizon can be shortened, while safety is guaranteed. This proves crucial for the experimental validation of the approach on a full size driverless race car. The vehicle in question won two international student racing competitions using the proposed framework; moreover, our hierarchical controller achieved an improvement of 20% in the lap time compared to the state of the art result achieved using a very similar car and track.
The presented paper can be downloaded here: arxiv.org/abs/2003.04882
_____________________________________________________
Visit our website and follow us on social media!
AMZ driverless Website: driverless.amzracing.ch
AMZ Racing on Facebook: facebook.com/amzracing
AMZ Racing on Twitter: twitter.com/amzracing?lang=en
AMZ Racing on Instagram: instagram.com/amzracing/?hl=en
AMZ Racing on LinkedIn: linkedin.com/company/akademischer-motorsportverein-z%C3%BCrich-amz-
