Documentation: Stanley controller algorithm for trajectory following

[mohitk3000]

Summary

Implement a Stanley Controller for trajectory following to improve lateral control performance in path tracking scenarios.

Background

The Stanley controller is a well-established lateral control algorithm, originally developed for the DARPA Grand Challenge by Stanford Racing Team. It computes steering commands based on:

• heading error between the robot and path
• lateral error between the robot and the nearest point on the trajectory

The original algorithm was proposed in this paper: https://ai.stanford.edu/~gabeh/papers/hoffmann_stanley_control07.pdf

I will be implementing a improved version of this(takes yaw stability also in account) which has been proposed in this paper:
https://www.mdpi.com/2076-0825/11/1/22

Proposed Tasks:

• Add a new sample program under src/simulations/control/ demonstrating Stanley-based trajectory following.

• Document the algorithm conceptually explaining heading_error, cross_track_error, gains, and how the control law combines them:

Labels

enhancement control trajectory-following proposal

Pls review this and suggest the improvements.

[ShisatoYano]

Hi @mohitk3000 ,
Thank you for proposing this issue, but my project has already had a simulation of Stanley controller as follow.
https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide/tree/main/src/simulations/path_tracking/stanley_path_tracking

So, in stead of it, I want to propose you can contribute by writing a documentation about algorithm and software design of the existing Stanley controller simulation.

What do you think??

[mohitk3000]

Yeah, sure.
I will raise PR for that.

[ShisatoYano]

@mohitk3000 Huge thanks to your understanding! Each existing documentation are located in the following doc directory.
https://github.com/ShisatoYano/AutonomousVehicleControlBeginnersGuide/tree/main/doc
Please create a new sub directory for path following algorithms and add the documentation about Stanley controller algorithms explanation.

[mohitk3000]

Here it is:
#70 (comment)

Look forward to your review and comments.