metadata = []
for height in np.linspace(max_height, min_height, num=heights):
print(f"Simulating {sims_per_height} drops from {height}m")
for i in range(sims_per_height):
# Set initial position and orientation of object
x = 0
y = 0
z = height
orientation = p.getQuaternionFromEuler((random.uniform(0, 2 * np.pi), random.uniform(0, 2 * np.pi), random.uniform(0, 2 * np.pi)))
p.resetBasePositionAndOrientation(obj_id, [x, y, z], orientation)
prev_linear_vel = np.zeros(3)
# Initialize the object position and velocity
pos_prev, orn_prev = p.getBasePositionAndOrientation(obj_id)
vel_prev, ang_vel_prev = p.getBaseVelocity(obj_id)
timestamp=0
dt=1/sample_freq
p.setTimeStep(dt)
filename=f"drop_{height}m_{i}.csv"
with open(f"output/{filename}", mode="w") as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['timestamp','accX','accY','accZ'])
while timestamp < sample_length:
p.stepSimulation()
linear_vel, angular_vel = p.getBaseVelocity(obj_id)
lin_acc = [(v - prev_v)/dt for v, prev_v in zip(linear_vel, prev_linear_vel)]
prev_linear_vel = linear_vel
timestamp += dt
# Get the current position and orientation of the object
pos, orn = p.getBasePositionAndOrientation(obj_id)
# Get the linear and angular velocity of the object in world coordinates
vel, ang_vel = p.getBaseVelocity(obj_id)
# Calculate the change in position and velocity between steps
pos_diff = np.array(pos) - np.array(pos_prev)
vel_diff = np.array(vel) - np.array(vel_prev)
# Convert the orientation quaternion to a rotation matrix
rot_matrix = np.array(p.getMatrixFromQuaternion(orn)).reshape(3, 3)
# Calculate the local linear acceleration of the object, subtracting gravity
local_acc = np.dot(rot_matrix.T, vel_diff / dt) - np.array([0, 0, -9.81])
# Restrict the acceleration to the range of the accelerometer
imu_rel_lin_acc_scaled = np.clip(local_acc, -range_acc, range_acc)
# Round the acceleration to the nearest resolution of the accelerometer
imu_rel_lin_acc_rounded = np.round(imu_rel_lin_acc_scaled/resolution_acc) * resolution_acc
# Update the previous position and velocity
pos_prev, orn_prev = pos, orn
vel_prev, ang_vel_prev = vel, ang_vel
# Save acceleration data to CSV file
with open(f"{output_folder}{filename}", mode="a") as csv_file:
writer = csv.writer(csv_file)
writer.writerow([timestamp*1000] + imu_rel_lin_acc_rounded.tolist())
nearestheight = round(height, 2)
metadata.append({
"path": filename,
"category": "training",
"label": { "type": "label", "label": str(nearestheight)}
})
This notebook takes you through a basic example of using the physics simulation tool PyBullet to generate an accelerometer dataset representing dropping the Nordic Thingy:53 devkit from different heights. This dataset can be used to train a regression model to predict drop height.
This idea could be used for a wide range of simulatable environments- for example generating accelerometer datasets for pose estimation or fall detection. The same concept could be applied in an FMEA application for generating strain datasets for structural monitoring.
There is also a video version of this tutorial:
