matlab-robotics-autonomy

MATLAB Robotics Autonomy

Use this skill for robotics, autonomy, and ROS workflows.

Workflow

1. Confirm environment: ROS or ROS 2, bag files, robot model, coordinate frames, sensors, and MATLAB toolboxes.
2. Load data or simulator deterministically.
3. Validate frames, units, timestamps, and message types before algorithms.
4. Build the smallest loop first: read data, process one frame, save one metric.
5. Scale to full trajectory, bag, or simulation only after the small loop passes.

Preferred APIs

• ROS: ros2bagreader, rosbagreader, message readers, publishers, subscribers.
• Robotics: rigidBodyTree, inverseKinematics, manipulatorRRT.
• Navigation: occupancy maps, planners, controllers, localization.
• Sensor fusion: Kalman filters, tracking filters, coordinate transforms.
• UAV: scenario, trajectory, and sensor simulation APIs when installed.

Acceptance Checks

Use at least one:

• Bag metadata and topic count parsed correctly.
• Frame transform chain is valid.
• Path has finite waypoints and no obvious obstacle collision.
• State estimator output has finite covariance.
• Trajectory tracking error is within tolerance.
• Figures or videos are exported with reproducible scripts.

Risk Checklist

Watch for:

• ENU/NED/body/world frame mixups.
• Degrees vs radians.
• Timestamp drift.
• Sensor rate mismatch.
• ROS domain ID or middleware mismatch.
• Large bag files that need sampled processing.

Tools

r = mas_kin_check(L, q);                    % FK + Jacobian vs finite difference
r = mas_kin_ik(L, pTarget, q0, opts);       % DLS IK, joint limits, step diagnostics
r = mas_ekf_check(model);                   % NEES / NIS consistency, Monte Carlo
r = mas_frame_check(T);                     % SE(3)/SO(3) hygiene, chain closure

Check the Jacobian against something that is not the Jacobian

mas_kin_check returns the analytic Jacobian and a central-difference Jacobian computed by differentiating the forward kinematics, then reports the disagreement. Every consumer of J -- resolved-rate control, IK, singularity maps, the manipulability ellipsoid, force/torque mapping -- is built on it, and a sign error or a dropped outer sum does not throw and does not look wrong on a plot of the arm; it produces a controller that drives the tip the wrong way.