Module model_predictive_trajectory_generator 

Model Predictive Trajectory Generator

Generates smooth trajectories using numerical optimization (Newton’s method) to connect an initial state to a target state. The trajectory is parameterized by arc length s, mid-point curvature km, and final curvature kf. A quadratic curvature profile interpolated from (k0, km, kf) is integrated via a bicycle kinematic model to produce the path. The Jacobian is computed numerically and used to iteratively refine the parameters until the terminal state error falls below a threshold.

Reference: https://github.com/AtsushiSakai/PythonRobotics/tree/master/PathPlanning/ModelPredictiveTrajectoryGenerator

Structs

LookupEntry

A single entry in the lookup table: (x, y, yaw, s, km, kf).

MptgConfig

Parameters for the trajectory generator.

MptgResult

Result of a successful trajectory optimization.

TargetState

A 2-D pose used as the optimization target.

Functions

generate_lookup_table

Generate a lookup table of pre-computed trajectory parameters.

optimize_trajectory

Run the trajectory optimization.

search_nearest_in_lookup_table

Search the lookup table for the entry closest (in Euclidean + yaw sense) to the query (tx, ty, tyaw).