Stanley Controller

Abstract

The Stanley controller is a non-linear controller for real-time autonomous automobile trajectory tracking. This repository contains a complete python abstraction of Stanford's Stanley controller.

Stanley Controller

At initialisation
:param control_gain:                (float) time constant [1/s]
:param softening_gain:              (float) softening gain [m/s]
:param yaw_rate_gain:               (float) yaw rate gain [rad]
:param steering_damp_gain:          (float) steering damp gain
:param max_steer:                   (float) vehicle's steering limits [rad]
:param wheelbase:                   (float) vehicle's wheelbase [m]
:param path_x:                      (ArrayLike) list of x-coordinates along the path
:param path_y:                      (ArrayLike) list of y-coordinates along the path
:param path_yaw:                    (ArrayLike) list of discrete yaw values along the path
:param dt:                          (float) discrete time period [s]

At every time step
:param x:                           (float) vehicle's x-coordinate [m]
:param y:                           (float) vehicle's y-coordinate [m]
:param yaw:                         (float) vehicle's heading [rad]
:param target_velocity:             (float) vehicle's velocity [m/s]
:param steering_angle:              (float) vehicle's steering angle [rad]

:return limited_steering_angle:     (float) steering angle after imposing steering limits [rad]
:return target_index:               (int) closest path index
:return crosstrack_error:           (float) distance from closest path index [m]

Stanley Controller Piecewise

For the sake of performance, it is recommended to pass NumPy arrays to the piecewise function to remove any conversion overheads in every frame. See here to understand the performance impact of array conversions.

At initialisation
:param control_gain:                (float) time constant [1/s]
:param softening_gain:              (float) softening gain [m/s]
:param yaw_rate_gain:               (float) yaw rate gain [rad]
:param steering_damp_gain:          (float) steering damp gain
:param max_steer:                   (float) vehicle's steering limits [rad]
:param wheelbase:                   (float) vehicle's wheelbase [m]
:param dt:                          (float) discrete time period [s]

Every frame
:param x:                           (float) vehicle's x-coordinate [m]
:param y:                           (float) vehicle's y-coordinate [m]
:param yaw:                         (float) vehicle's heading [rad]
:param target_velocity:             (float) vehicle's velocity [m/s]
:param steering_angle:              (float) vehicle's steering angle [rad]
:param path_x:                      (ndarray) piecewise list of x-coordinates along the path
:param path_y:                      (ndarray) piecewise list of y-coordinates along the path
:param path_yaw:                    (ndarray) piecewise list of discrete yaw values along the path

:return limited_steering_angle:     (float) steering angle after imposing steering limits [rad]
:return target_index:               (int) closest path index
:return crosstrack_error:           (float) distance from closest path index [m]

Useful Information

• This abstraction along with the original Stanley controller is based on forward driving. Thus, most computations are done relative to the vehicle's front axle.
• If you are passing path information piecewise, you have the advantage of increasing your control gain even further to minimise crosstrack error.

Requirements

pip install numpy

Demo

Recursively git clone the repository

git clone --recursive https://github.com/winstxnhdw/FullStanleyController.git

Install requirements.txt

pip install -r requirements.txt

Play the animation

python animation.py