robot sensor fusion architect

    1

    Architect safety-critical sensor fusion pipelines for autonomous robots, from data flow to failure mode handling.

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    robot sensor fusion architect

    Example session with this skill installed

    Design a sensor fusion plan for an outdoor delivery robot using LiDAR, stereo cameras, IMU, and GNSS. It must handle urban canyons where GNSS is unreliable.

    • Read your context and instructions
    • Compiled the robot sensor fusion

    I have architected a sensor fusion plan for your outdoor delivery robot, focusing on high-frequency pose estimation and robust GNSS-denied navigation.

    Robot Sensor-Fusion Architecture: Urban Delivery

    ComponentSensorRoleFailure Coverage
    LocalizationLiDAR (3D)PrimaryGNSS outages, low-light
    LocalizationGNSS (RTK)SupplementalDrift correction in open areas
    OdometryIMU (6-DOF)PrimaryShort-term LiDAR/GNSS loss
    ObstacleStereo CameraPrimaryTransparent/dark objects
    HealthDiagnosticsMonitorFault detection / Mode switching

    Fusion Logic & Timing

    • Core Algorithm: Error-State Extended Kalman Filter (ES-EKF) for state estimation (pose, velocity, IMU bias).
    **Coordinate Frames:** `map` -> `odom` -> `base_link`. LiDAR and Camera frames are statically transformed to `base_link` via extrinsic calibration parameters.
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    Timing: Master clock via PTP/NTP. Max latency budget for LiDAR-to-Fusion: 50ms. Data older than 100ms is dropped to prevent "ghost" obstacles.

    Degraded Modes

    • GNSS Loss: Switch to LiDAR-IMU odometry. Limit max speed to 1.5 m/s. Trigger "Degraded Localization" status.
    • LiDAR Failure: Immediate transition to "Safe Stop." Stereo cameras used for final 2D occupancy check before halt.
    • IMU Drift: Monitor Mahalanobis distance on innovation. If threshold exceeded, halt and request re-calibration.

    Validation Plan

    1. Extrinsic Cal: Use a checkerboard-target setup to align Camera/LiDAR extrinsics.
    2. Failure Injection: Deliberately disconnect GNSS antenna during a live run to verify EKF stability.
    3. Latency Benchmarking: Measure round-trip time from sensor interrupt to fusion output under full CPU load.

    Next steps

    • Finalize the noise covariance matrix for the IMU based on the manufacturer datasheet.
    • Define the static transform (URDF) for the stereo camera mounting pitch.
    • Perform a live verification run in a known urban canyon environment before full deployment.

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    About this skill

    The problem

    Designing sensor fusion for robotics is error-prone due to timing jitter, coordinate frame misalignment, and unhandled edge cases in degraded modes. Poorly architected pipelines lead to erratic autonomous behavior or catastrophic failures when a single sensor drifts or loses signal.

    What it does

    • Generates complete sensor role matrices including primary, redundant, and supplemental assignments.
    • Defines data flow architectures with specific latency budgets and synchronization tolerances.
    • Maps coordinate frame trees with extrinsic calibration strategies and uncertainty propagation.
    • Architects failure handling via degradation matrices and safe-state fallback logic.
    • Produces rigorous validation plans including hardware-in-the-loop and failure injection tests.

    Frameworks & tools

    Designed for ROS/ROS2, Kalman Filters (EKF/UKF), Factor Graphs, Protobuf/FastDDS, and hardware interfaces like LiDAR, IMU, and GNSS.

    Why this beats prompting it yourself

    Generic prompts often ignore the "safety-critical" reality of hardware, missing things like power rail cross-dependencies or stale data thresholds. This skill enforces a systematic engineering discipline that covers diagnostics, timing jitter, and mandatory real-system verification gates.

    Use cases

    • Architecting an outdoor UGV navigation stack with GNSS-denied fallback.
    • Designing redundant perception for warehouse AMR obstacle avoidance.
    • Building a drone state estimator combining IMU, optical flow, and UWB.
    • Creating a safety-rated diagnostic layer for industrial mobile platforms.

    Known limitations

    Requires detailed hardware specs for sensors. Does not write the low-level C++ or Python driver code itself.

    Requires specific hardware datasheets for accurate noise modeling. Does not generate final production C++/Python source code, only the architectural design and logic.

    How to use

    1. 1

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    What's inside

    Frequently Asked Questions

    The Architect provides high-level system designs, including data flow diagrams, sensor matrices, and failure mode logic, but it does not generate the final C++ or Python source code for the drivers.

    Yes, the Architect is designed to work with standard robotics middleware like ROS and ROS2, ensuring that coordinate frames, message types, and synchronization logic are compatible with these ecosystems.

    You will need to provide detailed hardware specifications, including sensor types (LiDAR, IMU, etc.), latency profiles, and your robot's intended operating environment.

    Unlike generic prompts, this skill enforces a safety-first engineering discipline, specifically addressing hardware-level issues like timing jitter, power rail dependencies, and stale data thresholds that general AI models often overlook.

    You receive a comprehensive sensor fusion blueprint, including data flow architectures, coordinate frame mappings, uncertainty propagation strategies, and a rigorous validation plan for hardware testing.

    The skill is highly effective for designing GNSS-denied navigation and redundant perception systems for AGVs, UGVs, drones, and industrial mobile platforms requiring safety-rated diagnostics.

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