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Conference Papers Year : 2021

Autonomous racecar control in head-to-head competition using Mixed-Integer Quadratic Programming

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Abstract

This work deals with the control of an autonomous racecar that should perform the fastest lap time on a track, while in presence of an opponent vehicle. Controlling the vehicle at its physical limit while ensuring collision-freeness is a challenging problem. We propose a Nonlinear Model Predictive Control (NMPC) model under a minimum time objective, which integrates the opponent vehicle's trajectory as a collision-avoidance constraint. By using a curvilinear coordinates system, progress time can be set as a direct optimization objective. The approximation of vehicle's shape is proposed and collision-avoidance constraints can therefore be represented efficiently. A safe control strategy is finally generated by a method based on Mixed-Integer Quadratic Programming (MIQP). We perform several experiments on our prototype implementation and discuss its performance issues.
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Dates and versions

hal-03749355 , version 1 (07-10-2022)

Identifiers

  • HAL Id : hal-03749355 , version 1

Cite

Nan Li, Eric Goubault, Laurent Pautet, S. Putot. Autonomous racecar control in head-to-head competition using Mixed-Integer Quadratic Programming. Opportunities and challenges with autonomous racing, 2021 ICRA workshop, May 2021, Online, United States. ⟨hal-03749355⟩
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