Trajectory Cost Evaluation¶

Scoring candidate paths for optimal selection.

In sampling-based controllers like DWA, dozens of candidate trajectories are generated at every time step. To choose the best one, Kompass uses a weighted sum of several cost functions.

The total cost \(J\) for a given trajectory is calculated as:

\[ J = \sum (w_i \cdot C_i) \]

Where \(w_i\) is the configured weight and \(C_i\) is the normalized cost value.

Hardware Acceleration¶

To handle high-frequency control loops with large sample sets, Kompass leverages SYCL for massive parallelism.

SYCL Kernels

Vendor-Agnostic GPU Support Each cost function below is implemented as a specialized SYCL kernel. This allows the controller to evaluate thousands of trajectory points in parallel on Nvidia, AMD, or Intel GPUs, significantly reducing latency compared to CPU-only implementations.

See the performance gains in our Benchmarks →

Benchmarking Results

Built-in Cost Functions¶

Kompass includes optimized implementations for the following cost components:

Cost Component	Description	Goal
Reference Path	Average distance between the candidate trajectory and the global reference path.	Stay on track. Keep the robot from drifting away from the global plan.
Goal Destination	Euclidean distance from the end of the trajectory to the final goal point.	Make progress. Favor trajectories that actually move the robot closer to the destination.
Obstacle Distance	Inverse of the minimum distance to the nearest obstacle (from LaserScan/PointCloud).	Stay safe. Heavily penalize trajectories that come too close to walls or objects.
Smoothness	Average change in velocity (acceleration) along the trajectory.	Drive smoothly. Prevent jerky velocity changes.
Jerk	Average change in acceleration along the trajectory.	Protect hardware. Minimize mechanical stress and wheel slip.

Configuration Weights¶

You can tune the behavior of the robot by adjusting the weights (\(w_i\)) in your configuration.

Name	Type	Default	Description
reference_path_distance_weight	`float`	`3.0`	Weight of the reference path cost. Must be between `0.0` and `1e3`.
goal_distance_weight	`float`	`3.0`	Weight of the goal position cost. Must be between `0.0` and `1e3`.
obstacles_distance_weight	`float`	`1.0`	Weight of the obstacles distance cost. Must be between `0.0` and `1e3`.
smoothness_weight	`float`	`0.0`	Weight of the trajectory smoothness cost. Must be between `0.0` and `1e3`.
jerk_weight	`float`	`0.0`	Weight of the trajectory jerk cost. Must be between `0.0` and `1e3`.

Tip

Setting a weight to 0.0 completely disables that specific cost calculation kernel, saving computational resources.