Velocity smoothing in ROS - Performance & Optimization

Performance: Velocity smoothing

MEDIUM IMPACT

Velocity smoothing affects the responsiveness and smoothness of robot motion commands, impacting real-time control and user experience.

Applying velocity commands to a robot for smooth motion

ROS

while (ros::ok()) {
  raw_velocity = getRawVelocityInput();
  smoothed_velocity = smoothVelocity(raw_velocity, previous_velocity);
  cmd_vel_pub.publish(smoothed_velocity);
  previous_velocity = smoothed_velocity;
  ros::spinOnce();
  loop_rate.sleep();
}

// smoothVelocity applies a low-pass filter to reduce sudden changes

Smoothing velocity commands reduces sudden jumps, lowering control noise and improving robot motion fluidity.

📈 Performance GainReduces control jitter and CPU spikes, improving real-time control stability.

Applying velocity commands to a robot for smooth motion

ROS

while (ros::ok()) {
  velocity = getRawVelocityInput();
  cmd_vel_pub.publish(velocity);
  ros::spinOnce();
  loop_rate.sleep();
}

Directly publishing raw velocity commands causes abrupt changes, leading to jerky robot movements and higher control jitter.

📉 Performance CostCauses frequent control oscillations, increasing CPU load and reducing real-time responsiveness.

Performance Comparison

Pattern	CPU Load	Control Responsiveness	Motion Smoothness	Verdict
Raw velocity commands	High due to oscillations	Low due to abrupt changes	Poor with jerky motion	[X] Bad
Smoothed velocity commands	Moderate with stable load	High with gradual changes	Good with fluid motion	[OK] Good

Rendering Pipeline

Velocity smoothing processes raw velocity inputs before publishing commands, affecting the control loop timing and robot actuator response.

→Input Processing

→Control Loop

→Command Publishing

⚠️ BottleneckControl Loop processing time can increase if smoothing is computationally heavy.

Optimization Tips

1Use simple filters like exponential moving average for velocity smoothing.

2Avoid complex smoothing algorithms that increase control loop delay.

3Always test smoothing impact on real-time responsiveness and robot behavior.

Performance Quiz - 3 Questions

What is the main performance benefit of applying velocity smoothing in ROS control loops?

A Increases maximum velocity achievable by the robot

B Decreases the number of messages published on the topic

C Reduces sudden velocity changes, improving control stability

D Eliminates the need for sensor feedback

DevTools: rqt_plot and rosbag

How to check: Record velocity commands and robot response using rosbag, then plot raw vs smoothed velocities with rqt_plot to compare smoothness and delay.

What to look for: Look for reduced spikes and gradual velocity changes in smoothed data indicating better performance.

Practice

1. What is the main purpose of velocity smoothing in ROS robot control?

easy

A. To gradually change speed and avoid sudden jumps

B. To increase the maximum speed instantly

C. To stop the robot immediately

D. To ignore acceleration limits

Solution

1. Step 1: Understand velocity smoothing concept

   Velocity smoothing means changing speed gradually to avoid sudden jumps.

2. Step 2: Identify the purpose in robot control

   This gradual change improves safety and comfort by preventing abrupt movements.

3. Final Answer:

   To gradually change speed and avoid sudden jumps -> Option A

4. Quick Check:

   Velocity smoothing = gradual speed change [OK]

Hint: Velocity smoothing means smooth speed changes, not instant jumps [OK]

Common Mistakes:

• Thinking velocity smoothing increases speed instantly
• Confusing smoothing with emergency stop
• Ignoring acceleration limits in smoothing

2. Which of the following is the correct Python function signature for a velocity smoothing function in ROS?

easy

A. def smooth_velocity(current_vel: float, target_vel: float, max_accel: float):

B. def smooth_velocity(current_vel, target_vel, max_accel):

C. def smooth_velocity(current_vel: int, target_vel: int, max_accel: int, dt: int):

D. def smooth_velocity(current_vel: float, target_vel: float, max_accel: float, dt: float) -> float:

Solution

1. Step 1: Check function parameters for velocity smoothing

   The function needs current velocity, target velocity, max acceleration, and time delta (dt) to calculate smoothing.

2. 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.

3. Final Answer:

   def smooth_velocity(current_vel: float, target_vel: float, max_accel: float, dt: float) -> float: -> Option D

4. Quick Check:

   Correct parameters and types = def smooth_velocity(current_vel: float, target_vel: float, max_accel: float, dt: float) -> float: [OK]

Hint: Include all needed parameters with correct types and return value [OK]

Common Mistakes:

• Missing dt parameter for time step
• Using int instead of float for velocities
• No return type annotation

3. Given the following code snippet for velocity smoothing, what will be the output if 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))

medium

A. 2.0

B. 3.0

C. 1.0

D. 4.0

Solution

1. Step 1: Calculate maximum allowed velocity change

   max_change = max_accel * dt = 1.0 * 1.0 = 1.0

2. Step 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 1.0

3. Step 3: Calculate new velocity

   new velocity = current_vel + delta = 1.0 + 1.0 = 2.0

4. Final Answer:

   2.0 -> Option A

5. Quick Check:

   Velocity change limited by max_accel * dt = 2.0 [OK]

Hint: Limit velocity change by max_accel * dt before adding [OK]

Common Mistakes:

• Adding full delta without limiting by max_change
• Returning target_vel directly
• Ignoring sign of delta

4. Identify the bug in this velocity smoothing function and choose the correct fix:

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 + delta

medium

A. Change max_change to max_accel / dt

B. Remove the if-else and always set delta = max_change

C. Change 'elif delta < max_change' to 'elif delta < -max_change'

D. Add abs() around delta in the if condition

Solution

1. Step 1: Analyze the conditions for limiting delta

   The function limits delta if it exceeds max_change positively or negatively.

2. 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.

3. Step 3: Correct the condition

   Change 'elif delta < max_change' to 'elif delta < -max_change' to correctly limit negative large changes.

4. Final Answer:

   Change 'elif delta < max_change' to 'elif delta < -max_change' -> Option C

5. Quick Check:

   Negative delta limit needs correct comparison [OK]

Hint: Check negative limit uses -max_change, not max_change [OK]

Common Mistakes:

• Using max_change instead of -max_change for negative check
• Removing conditions and causing wrong velocity jumps
• Incorrect calculation of max_change

5. You want to implement velocity smoothing for a robot that receives a list of target velocities every second: [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?

hard

A. [0, 1.5, 3.0, 4.5, 3.0]

B. [0, 1.5, 3.0, 3.0, 1.5]

C. [0, 1.5, 2.5, 3.0, 1.5]

D. [0, 2, 5, 3, 0]

Solution

1. 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 ≤1.5, velocity=3.0 - Step 5: target 0, delta=0-3=-3 < -1.5, velocity=3.0-1.5=1.5

2. Step 2: Compare with options

   The sequence [0, 1.5, 3.0, 3.0, 1.5] matches the calculated sequence.

3. Final Answer:

   [0, 1.5, 3.0, 3.0, 1.5] -> Option B

4. Quick Check:

   Apply max_accel limit each step = [0, 1.5, 3.0, 3.0, 1.5] [OK]

Hint: Apply max acceleration limit stepwise to each velocity [OK]

Common Mistakes:

• Using target velocities directly without smoothing
• Adding max_accel multiple times incorrectly
• Ignoring negative acceleration limits