analysis.md

How to Use the Output

1. Post-Simulation Analysis:

After running a simulation, you can load the .npy file using Python's NumPy library:

import numpy as np

# Load the trajectory data
trajectory_data = np.load('path_to_output/trajectory_test_600.npy')

# Extract positions and poses
positions = trajectory_data[:, :3]  # x, y, z positions
poses = trajectory_data[:, 3:]      # orientation data

This allows you to analyze the recorded data for detailed insights into the motion of the PBR.

2. Comparing Simulations:

When using parallel simulations, you can compare the .npy files from different namespaces to evaluate how different physics parameters affect the stability and movement of the PBR.

3. Error Analysis:

The .npy data allows for error analysis between the target (desired) and actual positions. By plotting these differences, you can assess how closely the simulation followed the intended motion pattern.

Example Visualization

The generated .npy file can be used to create plots of the PBR's motion over time. Below is an example of a simple visualization using Matplotlib:

import numpy as np
import matplotlib.pyplot as plt

# Load the recorded trajectory
data = np.load('path_to_output/trajectory_test_600.npy')

# Extract time, x, y, z positions
time = data[:, 0]  # Assuming the first column represents time
x, y, z = data[:, 1], data[:, 2], data[:, 3]

# Plot the trajectory
plt.figure()
plt.plot(time, x, label='X Position')
plt.plot(time, y, label='Y Position')
plt.plot(time, z, label='Z Position')
plt.xlabel('Time (s)')
plt.ylabel('Position (m)')
plt.title('PBR Trajectory Over Time')
plt.legend()
plt.grid(True)
plt.show()

This code snippet will generate a plot of the PBR's position over time, allowing you to visualize how it moved during the simulation.