README update

README.md

@@ -1,66 +1,89 @@
 # TSwR Projekt - MPC, iLQR, Stanley, Pure Pursuit
+## Description
+A project implementing four controllers for a vehicle in the AWSIM environment using ROS2 Humble.
 
-1. Wejście do kontenera -> run_x86.sh 
+## Installation Guide
+1. Enter the autoware container -> run_x86.sh 
 2. cd ~/autoware/src
 3. git clone https://github.com/mgmike1011/tswr_awsim
 4. cd ..
 5. colcon build --packages-select tswr_awsim
 6. . install/setup.bash
-7. Test działania: ros2 run tswr_awsim lin_MPC_controller 
-6. :)
-
-## iLQR:
-
-Run all comands inside docker
-
-Install required packages (Nie wiem jak to dodać żeby to rosdep zainstalował w sumie ewnwtualnie możemy to tak tu zostawić)
+## Controllers
+**Run all comands inside docker container**
+### iLQR
 
+#### Installing additional required packages
 ```bash
 pip install drake
 ```
-Start path publisher
+1. Start AWSIM simulator
+```console
+foo@bar:~/autoware$ cd f1tenth_simulators/F1Tenth_v0.5
+```
+```console
+foo@bar:~/autoware/f1tenth_simulators/F1Tenth_v0.5$ ./F1Tenth_v0.5.x86_64 
+```
+2. Start path publisher
 
-```python
-ros2 run tswr_awsim path_publisher
+```console
+foo@bar:~/autoware$ ros2 run tswr_awsim path_publisher
 ```
-Start iLQR planning algorithm
+3. Start iLQR planning algorithm
 
-```python
-ros2 run tswr_awsim iLQR_controller
+```console
+foo@bar:~/autoware$ ros2 run tswr_awsim iLQR_controller
 ```
 
-## Linearized Model Predictive Control:
+### Linearized Model Predictive Control:
 Computing steering angle and acceleration (with some limitations) based on current pose and a reference trajectory. The controller uses linearized model of vehicle kinematics to predict future state. It solves a discrete-time algebraic Riccati equation (DARE) to obtain the optimal feedback gain matrix K, which is used to compute optimal control input. 
 
-Run all comands inside docker container
-
-Start AWSIM simulator
+1. Start AWSIM simulator
 ```console
 foo@bar:~/autoware$ cd f1tenth_simulators/F1Tenth_v0.5
 ```
 ```console
 foo@bar:~/autoware/f1tenth_simulators/F1Tenth_v0.5$ ./F1Tenth_v0.5.x86_64 
 ```
 
-Start Linearized Model Predictive Control algorithm:
+2. Start Linearized Model Predictive Control algorithm:
 ```console
 foo@bar:~/autoware$ ros2 launch tswr_awsim lin_MPC_launch.py
 ```
 
-## Pure Pursuit:
+### Pure Pursuit:
 Computing steering angle using vehicle kinematics (with wheelbase 0.33 m), reference path and lookahaead distance 0.7 m. Equations correspond to a vehicle model with reference point in the center of the rear axle. 
 
-Run all comands inside docker containter 
-
-Start AWSIM simulator
+1. Start AWSIM simulator
 ```console
 foo@bar:~/autoware/f1tenth_simulators/F1Tenth_v0.5$ ./F1Tenth_v0.5.x86_64 
 ```
 
-Start Pure Pursuit algorithm:
+2. Start Pure Pursuit algorithm:
 ```console
 foo@bar:~/autoware$ ros2 launch tswr_awsim pure_pursuit_launch.py 
 ```
 
-
-
+### Stanley
+Computing steering angle using the heading error and cross-track error. In this method, the cross-track error is defined as the distance between the closest point on the path with the front axle of the vehicle.
+1. Start AWSIM simulator
+```console
+foo@bar:~/autoware/f1tenth_simulators/F1Tenth_v0.5$ ./F1Tenth_v0.5.x86_64 
+```
+2. Start Stanley algorithm:
+```console
+foo@bar:~/autoware$ ros2 launch tswr_awsim stanley_launch.py 
+```
+## Additional literature
+- [iLQR](https://jonathan-hui.medium.com/rl-lqr-ilqr-linear-quadratic-regulator-a5de5104c750)
+- [iLQR](https://github.com/Bharath2/iLQR)
+- [Pure Pursuit, Stanley](https://dingyan89.medium.com/three-methods-of-vehicle-lateral-control-pure-pursuit-stanley-and-mpc-db8cc1d32081)
+- [Stanley](http://ai.stanford.edu/~gabeh/papers/hoffmann_stanley_control07.pdf)
+
+## Authors
+- Agnieszka Piórkowska
+- Miłosz Gajewski
+- Maciej Mirecki
+- Mikołaj Zieliński
+
+Politechnika Poznańska 2023