Local NMPC on Global Optimised Path for Autonomous Racing
This addresses control challenges for autonomous racing cars, but it appears incremental as it combines existing methods like global path optimization and local NMPC.
The paper tackles autonomous racing by computing an optimal racing line and using a local nonlinear model predictive controller to follow it while handling objectives like progress, collision avoidance, and drafting, achieving unspecified performance improvements.
The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking track boundaries into account. With the optimal racing line as areference, a local nonlinear model predictive controller (NMPC) is proposed, which takes into account multiple local objectives like making more progress along the race line, avoiding collision with opponent vehicles, and use of drafting to achieve more progress.