Motion Profiling

A path tells the robot where to go, but it does not tell the robot how fast it should move. Motion profiling fills in that missing information by planning a target velocity and acceleration across the path.

You can think of a path as the shape of a racetrack and a motion profile as the driver’s plan. The track alone does not say where to accelerate, where to brake, or how quickly a corner can be taken. A motion profile answers those questions before the robot begins moving.

Why Use a Motion Profile?

Without a profile, the follower must make speed decisions reactively. It can notice that the robot is struggling in a curve and slow down, but by then the robot may already be moving too fast. A profile sees the curve ahead of time and begins braking early enough to enter it under control.

This provides several advantages:

• The robot can move close to its physical limits without regularly saturating its motors.
• Acceleration and braking become smoother and more repeatable.
• Feedforward can provide the expected motor power before an error develops.
• Velocity and acceleration constraints can be applied to different parts of a path.
• Less feedback correction is needed, leaving more power available for accurate movement.

Motion profiling is especially helpful when the localizer uses the drivetrain’s motor encoders. These encoders measure how far the wheels rotate, not how far the robot actually moves across the field. If a wheel slips while accelerating or taking a curve, the encoder still reports that rotation as movement and the estimated robot position becomes inaccurate.

A proper profile reduces this problem by keeping acceleration, cornering force, and total motor demand within limits the drivetrain can realistically produce. The wheels are less likely to break traction, so their encoder measurements remain a better estimate of real movement. A profile cannot prevent every slip caused by contact or field conditions, but it avoids creating unnecessary slip through overly aggressive commands.

There are two common ways to decide which point in a motion profile the robot should currently follow: time and displacement.

Time-Based Motion Profiling

A time-based profile assigns every target state to a time. For example, the robot might be expected to reach a curve after 1.5 seconds and finish the path after 3.0 seconds. As the clock advances, the target advances whether the robot keeps up or not.

This works well in a predictable environment. The problem appears when the real robot falls behind. A wheel can slip, the battery can sag, another robot can make contact, or a mechanism can change the robot’s weight distribution. The clock does not know that anything went wrong, so it continues moving the target down the path. The follower may then command a large correction to catch up, which can cause saturation or make the robot cut across the path.

Displacement-Based Motion Profiling

A displacement-based profile assigns every target state to a distance along the path. Instead of asking, “How long have we been moving?” the follower asks, “How far along the path are we?”

If the robot is pushed backward, its target does not race ahead without it. The follower continues using the velocity and acceleration planned for the robot’s current position. Once the disturbance is gone, the robot naturally continues through the rest of the profile.

Apex uses this approach. Its generated profile is stored in a lookup table indexed by distance along the path. During each update, the follower finds the robot’s progress and interpolates the correct target state from that table.

Why Displacement Wins in Competition

Time-based profiles can be excellent when the environment is controlled and following an exact schedule is more important than recovering from a disturbance. Competitive robotics is rarely that predictable. Field variation, wheel slip, battery voltage, collisions, and small localization errors all change how quickly a real robot completes a movement.

Displacement-based profiling keeps the physical plan attached to the path instead of the clock. The robot still slows down before the same curve, applies the same planned acceleration at the same location, and recovers without trying to make up lost time all at once. This makes it a better fit for fast autonomous movement in an environment where something unexpected can happen on any run.

Use Quick Builds when this planning is unnecessary, or continue to Profiled Builds to see how Apex generates a displacement-based profile.