robot sensor fusion architect

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.