from twist_controller import Controller # initialize the controller with sample parameters controller = Controller(kp=0.1, ki=0.01, kd=0.005, wheel_base=2.5, max_lat_accel=3., max_steer_angle=8.) # get the current state of the vehicle (e.g., from sensors or simulation) current_velocity = 10.0 # m/s angular_velocity = 0.1 # rad/s target_velocity = 12.0 # m/s target_angular_velocity = 0.0 # rad/s steer_value = 0.1 # rad # compute the control commands (throttle, brake, steering) based on the current state and target velocities throttle, brake, steering = controller.control(current_velocity, angular_velocity, target_velocity, target_angular_velocity, steer_value) # apply the control commands to the vehicle (e.g., using actuators or Gazebo ROS interface)
from pid import PID # import a PID controller implementation from a separate package (e.g., pid) # initialize the PID controller with sample gains and limits pid_controller = PID(Kp=0.1, Ki=0.01, Kd=0.005, output_limits=(-1., 1.)) # initialize the twist_controller with the PID controller and other parameters controller = Controller(pid_controller=pid_controller, wheel_base=2.5, max_lat_accel=3., max_steer_angle=8.) # apply the control commands to the vehicle for a given period of time (e.g., in a simulation) for t in range(1000): # get the current state of the vehicle (e.g., from sensors or simulation) current_velocity = ... angular_velocity = ... target_velocity = ... target_angular_velocity = ... steer_value = ... # compute the control commands based on the current state and target velocities throttle, brake, steering = controller.control(current_velocity, angular_velocity, target_velocity, target_angular_velocity, steer_value) # update the PID controller gains based on the error between the target and actual velocities error = target_velocity - current_velocity pid_controller.update(error) # apply the control commands to the vehicle (e.g., using actuators or Gazebo ROS interface) ...Both examples use Python and ROS libraries, such as `twist_controller` and `pid`, that are commonly used in robotics and autonomous vehicle development.