(02) Offline Trajectory Generation

• C++

  #include <ruckig/ruckig.hpp>
   
  #include "plotter.hpp"
   
   
  using namespace ruckig;
   
  int main() {
      // Create input parameters
      InputParameter<3> input;
      input.current_position = {0.0, 0.0, 0.5};
      input.current_velocity = {0.0, -2.2, -0.5};
      input.current_acceleration = {0.0, 2.5, -0.5};
   
      input.target_position = {5.0, -2.0, -3.5};
      input.target_velocity = {0.0, -0.5, -2.0};
      input.target_acceleration = {0.0, 0.0, 0.5};
   
      input.max_velocity = {3.0, 1.0, 3.0};
      input.max_acceleration = {3.0, 2.0, 1.0};
      input.max_jerk = {4.0, 3.0, 2.0};
   
      // Set different constraints for negative direction
      input.min_velocity = {-2.0, -0.5, -3.0};
      input.min_acceleration = {-2.0, -2.0, -2.0};
   
      // We don't need to pass the control rate (cycle time) when using only offline features
      Ruckig<3> ruckig;
      Trajectory<3> trajectory;
   
      // Calculate the trajectory in an offline manner (outside of the control loop)
      Result result = ruckig.calculate(input, trajectory);
      if (result == Result::ErrorInvalidInput) {
          std::cout << "Invalid input!" << std::endl;
          return -1;
      }
   
      // Get duration of the trajectory
      std::cout << "Trajectory duration: " << trajectory.get_duration() << " [s]." << std::endl;
   
      double new_time = 1.0;
   
      // Then, we can calculate the kinematic state at a given time
      std::array<double, 3> new_position, new_velocity, new_acceleration;
      trajectory.at_time(new_time, new_position, new_velocity, new_acceleration);
   
      std::cout << "Position at time " << new_time << " [s]: " << pretty_print(new_position) << std::endl;
   
      // Get some info about the position extrema of the trajectory
      std::array<Bound, 3> position_extrema = trajectory.get_position_extrema();
      std::cout << "Position extremas for DoF 4 are " << position_extrema[2].min << " (min) to " << position_extrema[2].max << " (max)" << std::endl;
  }

• Python

  from ruckig import InputParameter, Ruckig, Trajectory, Result
   
   
  if __name__ == '__main__':
      inp = InputParameter(3)
   
      inp.current_position = [0.0, 0.0, 0.5]
      inp.current_velocity = [0.0, -2.2, -0.5]
      inp.current_acceleration = [0.0, 2.5, -0.5]
   
      inp.target_position = [5.0, -2.0, -3.5]
      inp.target_velocity = [0.0, -0.5, -2.0]
      inp.target_acceleration = [0.0, 0.0, 0.5]
   
      inp.max_velocity = [3.0, 1.0, 3.0]
      inp.max_acceleration = [3.0, 2.0, 1.0]
      inp.max_jerk = [4.0, 3.0, 2.0]
   
      # Set different constraints for negative direction
      inp.min_velocity = [-1.0, -0.5, -3.0]
      inp.min_acceleration = [-2.0, -1.0, -2.0]
   
      # We don't need to pass the control rate (cycle time) when using only offline features
      otg = Ruckig(3)
      trajectory = Trajectory(3)
   
      # Calculate the trajectory in an offline manner
      result = otg.calculate(inp, trajectory)
      if result == Result.ErrorInvalidInput:
          raise Exception('Invalid input!')
   
      print(f'Trajectory duration: {trajectory.duration:0.4f} [s]')
   
      new_time = 1.0
   
      # Then, we can calculate the kinematic state at a given time
      new_position, new_velocity, new_acceleration = trajectory.at_time(new_time)
   
      print(f'Position at time {new_time:0.4f} [s]: {new_position}')
   
      # Get some info about the position extrema of the trajectory
      print(f'Position extremas are {trajectory.position_extrema}')

Output Trajectory