Update README.md

README.md

@@ -1,9 +1,8 @@
 # Planning-and-MPC
 
-Let's see what we can learn about MPC and RRT*
-
-This repository assumes that you have this [repo](https://github.com/pmusau17/Platooning-F1Tenth) cloned and built. This repository contains implementations of planning algorithms for the F1Tenth Platform. I'm going to start writing tutorials on this as I learn. 
+Let's see what we can learn about MPC and RRT*. 
 
+This repository assumes that you have this [repo](https://github.com/pmusau17/Platooning-F1Tenth) cloned and built. You can either install it on your host or run it using docker. The dockerfiles and instructions for getting everything setup can be found a the above link. I will try and make sure that everything in this repository can be run through a container. Mostly this reposityory will contain implementations of planning algorithms and model predictive control regimes as I learn them. The idea is to write tutorials as I go (If I haven't done this yet when you arrive please remind me :) ).
 
 # RRT*:
 ![RRT_Biased](images/rrt_normal.gif)
@@ -48,8 +47,23 @@ and then launch the rsband_local_planner
 
 Launch the [F1Tenth Simulator](https://github.com/pmusau17/Platooning-F1Tenth): 
 
+If you have the docker image built run the following 
+
+
+```
+$ ./run_docker_simulator.sh
 ```
-roslaunch race move_base_planning.launch
+In the docker terminal run: 
+```
+source devel/setup.bash && roslaunch race move_base_planning.launch
+```
+
+This should bring up a simulation of a racecar and some cones placed randomly throughout the racetrack. 
+
+In another terminal run:
+
+```
+$ ./run_docker.sh
 ```
 
 In the docker terminal: 
@@ -58,4 +72,8 @@ In the docker terminal:
 $ roslaunch rsband_local_planner move_base_planning.launch
 ```
 
-You can then set goal poses in rviz
+This should launch the following rviz window: 
+
+![following_local_plan](images/following_local_plan.gif)
+
+In the above gif, the plans are generated using the rsband local planner proposed by [George Kouros](https://github.com/gkouros/rsband_local_planner) and tracked using a [pure pursuit controller](rsband_local_planner/scripts/pure_pursuit.py). The goals are sent by a node that is a rough implementation of a behaviorial layer. The idea here is that whenever the car gets within a 1 m radius of the goal point we say that the plan has been executed and send the next goal point. The behavioral node implementation node can be found [here](rsband_local_planner/scripts/behavioral_layer.py). The costmaps used throught the planning are updated using the vehicle's lidar sensor and make use of the [move_base](http://wiki.ros.org/move_base) ros package. The configuration files (these were a paaaaaaiiiiin) can be found [here](https://github.com/pmusau17/Planning-and-MPC/tree/main/rsband_local_planner/cfg).