IAC-Control-Strategies

Currently used approach :-

Step 1: Use bayesian optimization to find the global optimal racing line and save the coordinates for the trajectory generated in a text file. References :- https://arxiv.org/pdf/2002.04794.pdf

Step 2: Run MPC on the global trajectory generated to locally avoid obstacles and perform overtaking maneuver with competitor vehicles. References :- Model-predictive active steering and obstacle avoidance for autonomous ground vehicles, Optimization‐based autonomous racing of 1:43 scale RC cars, “Kinematic and Dynamic Vehicle Models for Autonomous Driving Control Design” ,Jason Kong , Mark Pfeiffer, Georg Schildbach , Francesco Borrelli

Prerequisites

1. Install rtidds-connext, casadi python libraries using pip install

For hackathon-2, make the following changes as per the old configuration

1. Switch back to Indy_scheduled configuration
2. Change the directory for controller process (Point it to the new location of ds_control__controller.exe from DS_DDS folder
3. Remove the extra arguments
4. Run the controller scripts from MPC folder

For hackathon-3, make the following changes as per the new configuration

1. Swith the configuration to hack3_1ego for 1 ego vehicle, hack3_2ego for 2 ego vehicles
2. Comment 2 lines in cnxwrapper.cpp (Refer to the video)
3. Open scade and open scade_controller project
4. Change the id constant to 1
5. Swith the build method to DS_DDS1
6. Check the topic names. If they do not already contain _ego1 as suffix, add it by going to tools->update topicnames and eecute the command '-o Control::Controller -s _ego1'
7. Rebuild the project to generate the new ds_control__controller.exe file
8. Launch the controller scripts from MPC_ego1 folder

Refer to the following video : Link