Example 02: Offline Trajectory Generation

C++

#include <iostream>
 
#include <ruckig/ruckig.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> otg;
    Trajectory<3> trajectory;
 
    // Calculate the trajectory in an offline manner (outside of the control loop)
    Result result = otg.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]: " << join(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