opt_mintime

opt_mintime(reftrack, coeffs_x, coeffs_y, normvectors, pars, tpamap_path, tpadata_path, export_path, print_debug=False, plot_debug=False)

Created by: Fabian Christ

Extended by: Thomas Herrmann, Francesco Passigato

Documentation: The minimum lap time problem is described as an optimal control problem, converted to a nonlinear program using direct orthogonal Gauss-Legendre collocation and then solved by the interior-point method IPOPT. Reduced computing times are achieved using a curvilinear abscissa approach for track description, algorithmic differentiation using the software framework CasADi, and a smoothing of the track input data by approximate spline regression. The vehicles behavior is approximated as a double track model with quasi-steady state tire load simplification and nonlinear tire model.

Please refer to our paper for further information: Christ, Wischnewski, Heilmeier, Lohmann Time-Optimal Trajectory Planning for a Race Car Considering Variable Tire-Road Friction Coefficients

Inputs: reftrack: track [x_m, y_m, w_tr_right_m, w_tr_left_m] coeffs_x: coefficient matrix of the x splines with size (no_splines x 4) coeffs_y: coefficient matrix of the y splines with size (no_splines x 4) normvectors: array containing normalized normal vectors for every traj. point [x_component, y_component] pars: parameters dictionary tpamap_path: file path to tpa map (required for friction map loading) tpadata_path: file path to tpa data (required for friction map loading) export_path: path to output folder for warm start files and solution files print_debug: determines if debug messages are printed plot_debug: determines if debug plots are shown

Outputs: alpha_opt: solution vector of the optimization problem containing the lateral shift in m for every point v_opt: velocity profile for the raceline reftrack: possibly (depending on non-regular sampling) modified reference track must be returned for later usage a_interp: possibly (depending on non-regular sampling) modified equation system matrix for splines must be returned for later usage normvectors: possibly (depending on non-regular sampling) modified normal vectors must be returned for later usage