Readme updated

README.md

@@ -28,17 +28,17 @@ Here you can find the main Autonomous Systems controller for CAT15x, the [BCN eM
 If you want to see the performance of this controller watch [THIS TRACKDRIVE](https://youtu.be/mk9U0lRWr-0?si=S0-yVm7wfKk2jvPq).
 
 <div align="center">
-<a> <img src="docs/mpc_black.png" alt="Logo" width="855"> </a>
+<a> <img src="docs/autocross_short.gif" alt="Logo" width="855"> </a>
 <!-- <h3 align="center">Tailored MPC</h3> -->
 </div>
 <br />
 
-This software is shared as part of my [Final Degree Thesis](docs/tfg_oriolmartinez.pdf). The controller actually used for competing during the 2022-2023 Formula Student season has been the `lateral` approach, so it's the one explained in the thesis (and the one that is driving the car in the [trackdrive video](https://youtu.be/mk9U0lRWr-0?si=S0-yVm7wfKk2jvPq)). 
+This software is shared as part of my [Final Degree Thesis](http://hdl.handle.net/2117/405362). The controller actually used for competing during the 2022-2023 Formula Student season has been the `lateral` approach, so it's the one explained in the thesis (and the one that is driving the car in the [trackdrive video](https://youtu.be/mk9U0lRWr-0?si=S0-yVm7wfKk2jvPq)). 
 
 Both other architectures were left apart due to the added tuning complexity of coupled NMPCs. However, the  `master` approach is ready to drive :). The `spatial` approach is the one I found the most difficult to tune so it's not really fine tuned at the moment.
 
 ## Disclaimer
-This is a tailored control solution made for the CAT15x Formula Student vehicle. In order to make a proper use of this algorithm, it's the user duty to make sure the dynamic model (presented [here](docs/tfg_oriolmartinez.pdf)) actually approximates the behaviour of the car. 
+This is a tailored control solution made for the CAT15x Formula Student vehicle. In order to make a proper use of this algorithm, it's the user duty to make sure the dynamic model (presented [here](http://hdl.handle.net/2117/405362)) actually approximates the behaviour of the car. 
 
 If you use this control algorithm in a Formula Student competition the **only** thing I ask for is to **ALWAYS REFERENCE** the team ___BCN eMotorsport___.
 
@@ -51,7 +51,7 @@ If you use this control algorithm in a Formula Student competition the **only**
 
 ## Approach
 
-For specific information on how the lateral controller work read [Tailored MPC](docs/tfg_oriolmartinez.pdf)'s paper.
+For specific information on how the lateral controller work read [Tailored MPC](http://hdl.handle.net/2117/405362)'s paper.
 
 For the sake of simplicity the different controllers are named after their more important characteristic. However, all the specified MPC controllers are curvature-based and follow a simplified non linear bicycle model.
 
@@ -129,4 +129,4 @@ Explanation of all parameters from the lateral branch:
 
 
 ## Results
-The technical performance of the controller is discussed [here](docs/tfg_oriolmartinez.pdf).
+The technical performance of the controller is discussed [here](http://hdl.handle.net/2117/405362).

src/main.cpp

@@ -104,7 +104,7 @@ int main(int argc, char **argv) {
         mpc.solve(); // Solve the NLOP
 
         mpc.msgCommands(&msg);
-        if(mpc.forces.exit_flag == 1 /*|| mpc.forces.exit_flag == 0*/ ) pubCommands.publish(msg); // publish car commands
+        if(mpc.forces.exit_flag == 1 || mpc.forces.exit_flag == 0 ) pubCommands.publish(msg); // publish car commands
 
         // DEBUG
         float_msg.data = mpc.elapsed_time.count()*1000;