This is a ROS-based C++ simulation environment for a two-wheeled inverted pendulum (Segway) robot. It currently has two branches, a Model Predictive Control (MPC) branch that uses a Mixed Observable Markov Decision Process (MOMDP) planner for high-level planning, and uses the MPC for trajectory generation and control. The other branch is a Control Barrier Function (CBF) branch that guarantees that the system stays in a "safe" subset of the state space, regardless of the inputs given to the system from the user.

Eigen is required. If Eigen is not installed, or you want to run the version of Eigen that runs on the Segway, use the Eigen_embedded folder and replace all instances of Eigen_embedded with Eigen.

The OSQP solver is used for both branches, and can be found here

The MPC branch requires the following library for MPC, as well as the following ROS package for its message structure.

CBF branch installation coming soon.

The simulation is based on the Ninebot Elite used in the AMBER lab link. The code run in this simulation environment has been used directly on the hardware, as evidenced in this

After intalling the MPC and OSQP libraries listend in the above Prerequisites section follows the following steps:

• Run the main.py file in the folder /segway_sim/src/pyFun. It should save a .pkl object in the /segway_sim/src/pyFun/data/Qug_1 folder
• Download the ambercorted_ros pakage from here
• Checkout the dev branch in the ambercorted_ros package
• Build your catkin workspace
• Open the launch file main.launch. At the bottom of the launch file enter the folder where you would like to save your bag file
• Run roslaunch segway_sim main.launch
• In a new terminal run rosservice call /segway_sim/integrator/ui "cmd: 1"
• After the simulation run the file readNewBag.py in the /segway_sim/plotUtils folder (it is needed to change the path where the bag file has been saved)

Coming soon

This code is based on the following:

(CBF)

• Gurriet, T., Singletary, A., Reher, J., Ciarletta, L., Feron, E., & Ames, A. (2018, April). Towards a framework for realizable safety critical control through active set invariance. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS) (pp. 98-106). IEEE. PDF
• U. Rosolia and A. D. Ames, "Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies," in IEEE Control Systems Letters, vol. 5, no. 3, pp. 1007-1012, July 2021, PDF