Velocity smoothing helps a robot move smoothly without sudden jumps in speed. It makes the robot's motion safer and more natural.
Velocity smoothing in ROS
def smooth_velocity(current_velocity, target_velocity, max_acceleration, dt): velocity_diff = target_velocity - current_velocity max_change = max_acceleration * dt if abs(velocity_diff) > max_change: velocity_diff = max_change if velocity_diff > 0 else -max_change new_velocity = current_velocity + velocity_diff return new_velocity
This function takes the current speed and target speed, then changes the speed gradually.
max_acceleration limits how fast the speed can change per second.
smooth_velocity(0.0, 1.0, 0.5, 1.0) # returns 0.5
smooth_velocity(0.5, 1.0, 0.5, 0.5) # returns 0.75
smooth_velocity(0.9, 1.0, 0.5, 1.0) # returns 1.0
This program shows how velocity changes smoothly from 0 to 2 over time, increasing by at most 0.5 units per second.
import time def smooth_velocity(current_velocity, target_velocity, max_acceleration, dt): velocity_diff = target_velocity - current_velocity max_change = max_acceleration * dt if abs(velocity_diff) > max_change: velocity_diff = max_change if velocity_diff > 0 else -max_change new_velocity = current_velocity + velocity_diff return new_velocity # Simulate velocity smoothing over 5 seconds current_velocity = 0.0 target_velocity = 2.0 max_acceleration = 0.5 # units per second squared print('Time(s) | Velocity') for i in range(6): print(f'{i:6} | {current_velocity:.2f}') current_velocity = smooth_velocity(current_velocity, target_velocity, max_acceleration, 1.0) time.sleep(0.1) # just to slow output, not needed in real ROS node
Velocity smoothing helps protect motors and gears from sudden shocks.
In ROS, this logic is often used inside a control loop publishing velocity commands.
Adjust max_acceleration to make motion more or less smooth.
Velocity smoothing gradually changes speed to avoid sudden jumps.
It improves safety and comfort in robot movement.
Use a simple function to limit speed changes based on max acceleration and time.
Practice
Solution
Step 1: Understand velocity smoothing concept
Velocity smoothing means changing speed gradually to avoid sudden jumps.Step 2: Identify the purpose in robot control
This gradual change improves safety and comfort by preventing abrupt movements.Final Answer:
To gradually change speed and avoid sudden jumps -> Option AQuick Check:
Velocity smoothing = gradual speed change [OK]
- Thinking velocity smoothing increases speed instantly
- Confusing smoothing with emergency stop
- Ignoring acceleration limits in smoothing
Solution
Step 1: Check function parameters for velocity smoothing
The function needs current velocity, target velocity, max acceleration, and time delta (dt) to calculate smoothing.Step 2: Verify correct typing and return type
Using floats for velocities and acceleration is correct, and the function returns a float for new velocity.Final Answer:
def smooth_velocity(current_vel: float, target_vel: float, max_accel: float, dt: float) -> float: -> Option DQuick Check:
Correct parameters and types = def smooth_velocity(current_vel: float, target_vel: float, max_accel: float, dt: float) -> float: [OK]
- Missing dt parameter for time step
- Using int instead of float for velocities
- No return type annotation
current_vel = 1.0, target_vel = 3.0, max_accel = 1.0, and dt = 1.0?
def smooth_velocity(current_vel, target_vel, max_accel, dt):
max_change = max_accel * dt
delta = target_vel - current_vel
if abs(delta) > max_change:
delta = max_change if delta > 0 else -max_change
return current_vel + delta
print(smooth_velocity(1.0, 3.0, 1.0, 1.0))Solution
Step 1: Calculate maximum allowed velocity change
max_change = max_accel * dt = 1.0 * 1.0 = 1.0Step 2: Calculate delta and limit it
delta = target_vel - current_vel = 3.0 - 1.0 = 2.0, which is greater than max_change, so delta is limited to 1.0Step 3: Calculate new velocity
new velocity = current_vel + delta = 1.0 + 1.0 = 2.0Final Answer:
2.0 -> Option AQuick Check:
Velocity change limited by max_accel * dt = 2.0 [OK]
- Adding full delta without limiting by max_change
- Returning target_vel directly
- Ignoring sign of delta
def smooth_velocity(current_vel, target_vel, max_accel, dt):
max_change = max_accel * dt
delta = target_vel - current_vel
if delta > max_change:
delta = max_change
elif delta < max_change:
delta = -max_change
return current_vel + deltaSolution
Step 1: Analyze the conditions for limiting delta
The function limits delta if it exceeds max_change positively or negatively.Step 2: Identify incorrect condition
The condition 'elif delta < max_change' is wrong because it triggers for any delta less than max_change, including values that don't need limiting. It should check if delta is less than negative max_change.Step 3: Correct the condition
Change 'elif delta < max_change' to 'elif delta < -max_change' to correctly limit negative large changes.Final Answer:
Change 'elif delta < max_change' to 'elif delta < -max_change' -> Option CQuick Check:
Negative delta limit needs correct comparison [OK]
- Using max_change instead of -max_change for negative check
- Removing conditions and causing wrong velocity jumps
- Incorrect calculation of max_change
[0, 2, 5, 3, 0]. The robot's max acceleration is 1.5 m/s² and the time step is 1 second. Which sequence of smoothed velocities will correctly apply velocity smoothing starting from 0 m/s?Solution
Step 1: Calculate smoothed velocities step-by-step
Start at 0 m/s. Max change per step = 1.5 m/s. - Step 1: target 0 -> 0 (start) - Step 2: target 2, delta=2-0=2 >1.5, so velocity=0+1.5=1.5 - Step 3: target 5, delta=5-1.5=3.5 >1.5, velocity=1.5+1.5=3.0 - Step 4: target 3, delta=3-3=0 ā¤1.5, velocity=3.0 - Step 5: target 0, delta=0-3=-3 < -1.5, velocity=3.0-1.5=1.5Step 2: Compare with options
The sequence [0, 1.5, 3.0, 3.0, 1.5] matches the calculated sequence.Final Answer:
[0, 1.5, 3.0, 3.0, 1.5] -> Option BQuick Check:
Apply max_accel limit each step = [0, 1.5, 3.0, 3.0, 1.5] [OK]
- Using target velocities directly without smoothing
- Adding max_accel multiple times incorrectly
- Ignoring negative acceleration limits
Ready to prove it?
Pass the test and this pattern moves unknown
