![]() ![]() We need to modify this behavior in order to drive the vehicle from one point to another. A topic that outputs the result given the input parameters.A topic that waits for the input parameters of the optimizer.The auto generated ROS package (as stated in the documentation) creates two topics: Roslaunch husky_gazebo husky_empty_world.launch Edit the auto-generated node Now we can run the Husky simulation as follows Download and run Husky packages cd ~/open_ros_codegen/catkin_ws/src Now we must edit it a little bit to drive our vehicle. Right now we have a running ROS node with the defualt configuration. Roslaunch open_nmpc_controller open_optimizer.launch Source ~/open_ros_codegen/catkin_ws/devel/setup.bash Ln -s ~/open_ros_codegen/nmpc_open/optimization_engine/mpc_controller/open_nmpc_controller. mkdir -p ~/open_ros_codegen/catkin_ws/src After that, we can build and test our package. We will do this by creating a symbolic link. We must create a ROS catkin workspace and place our package inside the src folder. Create a ROS workspace for our new package The ROS package will be in optimization_engine/mpc_controller/open_nmpc_controller. The above program will generate a parametric optimizer at optimization_engine/mpc_controller. Solver_config = og.config.SolverConfiguration()\īuilder = og.builder.OpEnOptimizerBuilder(problem, with_build_directory( "optimization_engine")\ with_node_name( "open_mpc_controller_node") \īuild_config = og.config.BuildConfiguration()\ with_package_name( "open_nmpc_controller") \ Ros_config = og.config.RosConfiguration() \ Problem = og.builder.Problem(optimization_variables, Umax = * N # cs.DM.ones(NU * N) * cs.infīounds = og.constraints.Rectangle(umin, umax) Umin = * N # - cs.DM.ones(NU * N) * cs.inf Optimization_parameters = cs.vertcat(*optimization_parameters) Optimization_variables = cs.vertcat(*optimization_variables) Total_cost += terminal_cost(x_t, x_ref) # terminal cost X_t = dynamics_dt(x_t, u_k) # update state Total_cost += stage_cost(x_t, u_k, x_ref) # update cost ![]() # The terminal cost for x def terminal_cost (_x, _x_ref=None): if _x_ref is None: # The stage cost for x and u def stage_cost (_x, _u, _x_ref=None, _u_ref=None): if _x_ref is None: Return cs.vcat( + sampling_time * dx for i in range(NX)]) QN = cs.DM.eye(NX) * ĭef dynamics_ct (_x, _u): return cs.vcat( * cs.cos(_x), NU = 2 # The number of elements in the control vector NX = 3 # The number of elements in the state vector Let's make a folder for the code generation script mkdir -p ~/open_ros_codegen/nmpc_openĬreate a file named create_open_solver.py inside the nmpc_open folder with the following content import casadi as cs ![]() Install opengen 0.5.0 in a virtual environment cd ~ You will have to provide certain configuration parameters, such as the package name, the node name and the rate of your node in Hz. To generate a ROS package you can use opengen - OpEn's Python interface (with opengen version 0.5.0 or newer). The auto generated controller will allow you to move the vehicle to any point of the simulation grid, as shown in the following video Code generation for the MPC controller ![]()
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