Software That Moves Things in the Real World
Robotics software is unique: it controls physical actuators (motors, hydraulics, pneumatics) that interact with the real world. A bug in a web application shows an error page. A bug in a robot controller can: crash a robotic arm into a workpiece, drive an autonomous vehicle off the road, or cause a surgical robot to make an incorrect movement. The physical consequences of software errors make robotics governance critical.
Safety standards: ISO 10218 (Industrial robots — safety requirements), ISO 15066 (Collaborative robots — safety for human-robot interaction), IEC 61508 (Functional safety of electrical/electronic systems), and domain-specific standards (ISO 13482 for personal care robots, ISO 8373 for robot vocabulary and classifications). AI rule: 'Every robotics feature must be evaluated against the applicable safety standard. The ASIL/SIL level determines development rigor. Safety-critical functions (emergency stop, collision avoidance, force limiting) require the highest verification level.'
The governance framework: robotics AI rules must enforce: safety constraints (the robot must never exceed safe force/speed/position limits), real-time guarantees (control loops must execute within their deadline), sensor validation (sensor data must be validated before acting on it), and fail-safe behavior (when something goes wrong, the robot stops safely, it does not flail unpredictably).
Real-Time Control and Timing Guarantees
Robot control loops run at fixed frequencies: position control at 1kHz (1ms cycle), force control at 500Hz-10kHz, vision processing at 30-60Hz, path planning at 10-100Hz. Missing a control loop deadline: the robot may overshoot its target, apply incorrect force, or lose track of its position. AI rule: 'Control loop code: hard real-time. No dynamic memory allocation, no garbage collection, no blocking I/O, no system calls that can cause unpredictable delays. Pre-allocate all buffers. Use fixed-size data structures.'
ROS (Robot Operating System): the de facto framework for robotics development. ROS 2 uses DDS (Data Distribution Service) for real-time communication between nodes. AI rules for ROS 2: 'Use lifecycle nodes for predictable startup/shutdown. Set QoS profiles appropriate for the data type (reliable for commands, best-effort for high-frequency sensor data). Use ROS 2 real-time executor for control loops. Generate node parameters as configurable (not hardcoded) for tuning.'
Watchdog timers: if the control loop fails to execute within its deadline, a hardware watchdog timer triggers an emergency stop. AI rule: 'Every control loop: feed the hardware watchdog at the start of each cycle. If the loop hangs or crashes: the watchdog timer expires and triggers e-stop. This is the last line of defense against control software failure. Never disable the watchdog for debugging without a physical safety mechanism in place.'
malloc() in a control loop running at 1kHz is a time bomb. Memory allocation time is non-deterministic — it might take 1 microsecond or 10 milliseconds depending on heap fragmentation. In a 1ms control cycle: a 10ms allocation means the loop misses 10 deadlines. The watchdog fires. The robot e-stops. Pre-allocate all buffers at startup. Use fixed-size arrays. The AI must never generate new/malloc/vector resize inside a real-time control loop.
Sensor Fusion and Safety Constraints
Sensor validation: robots use multiple sensors (encoders, IMUs, LiDAR, cameras, force/torque sensors). Sensor data must be validated before acting on it. A faulty encoder reading: could cause the robot to believe it is in a different position than reality. AI rule: 'Every sensor reading: validate against expected range (is the value physically possible?), check for freshness (is the data recent enough?), cross-validate with redundant sensors (does the encoder position match the vision estimate?). Invalid sensor data: halt motion and report the fault.'
Safety constraints as code: the robot must enforce safety limits in software (and preferably in hardware too). Maximum joint velocities, maximum forces, workspace boundaries (the robot cannot move outside its safe workspace), and collision zones (the robot slows or stops when a human is within the collaborative zone). AI rule: 'Generate safety constraint checking in the control loop: before commanding any motion, verify that the commanded position/velocity/force is within safe limits. If the command exceeds limits: clamp to the safe value and log the limit violation.'
Emergency stop (e-stop): the highest priority safety function. When triggered: all actuators are de-energized (or brought to a controlled stop for heavy robots where sudden de-energization is unsafe). AI rule: 'E-stop: always available, never bypassed, highest interrupt priority. The AI must never generate code that disables or delays e-stop processing. E-stop state: latched (requires manual reset after the condition is cleared). Generate e-stop monitoring and state management in every robot control system.'
A single encoder says the arm is at 45 degrees. Is it really? Cross-validate: does the IMU orientation match? Does the vision system see the arm at approximately 45 degrees? Does the motor current correspond to the expected load at 45 degrees? If three of four sensors agree and one disagrees: the disagreeing sensor is likely faulty. This redundancy-based fault detection catches hardware failures before they cause dangerous behaviors.
Simulation and Testing Requirements
Simulation before hardware: robotics code must be tested in simulation before running on physical hardware. Gazebo, Isaac Sim, MuJoCo: provide physics simulation for testing robot behaviors. AI rule: 'Generate simulation-compatible code alongside physical code. Use ROS 2 abstractions that work with both simulated and real hardware. Test all motion commands, safety constraints, and fault handling in simulation before deploying to physical robots.'
Hardware-in-the-loop (HIL) testing: the control software runs on the real controller hardware but connected to a simulated plant (robot model). This tests: real-time timing, hardware interfaces, and controller performance. AI rule: 'Before deploying control code to a physical robot: run HIL tests that verify timing constraints, sensor processing latency, and actuator command rates. Log timing metrics (loop execution time, jitter) for every test run.'
Regression testing for safety: every code change to safety-critical functions must re-run the full safety test suite. AI rule: 'Safety-critical code changes: full regression test including: e-stop response time, force limit enforcement, workspace boundary enforcement, and fault detection accuracy. Generate automated safety test scenarios that exercise all safety constraints. No safety code change ships without passing the full safety regression suite.'
Testing a new motion command directly on a physical robot risks: collisions, damage to the robot or its environment, and injury to nearby people. Testing in simulation first: catches 90% of bugs with zero physical risk. The simulation workflow: develop in simulation → pass all safety tests in simulation → deploy to HIL testing → pass timing and interface tests → only then deploy to physical robot with safety observers present.
Robotics AI Governance Summary
Summary of AI governance rules for robotics software development teams.
- Physical consequences: bugs cause real-world harm. Safety standards (ISO 10218, 15066, IEC 61508) apply
- Real-time: hard deadlines for control loops. No dynamic allocation, no GC, no blocking I/O
- ROS 2: lifecycle nodes, appropriate QoS, real-time executor, configurable parameters
- Watchdog: hardware watchdog triggers e-stop if control loop misses deadline
- Sensor validation: range check, freshness check, cross-validation. Invalid data = halt motion
- Safety constraints: position/velocity/force limits checked before every actuator command
- E-stop: highest priority, never bypassed, latched state, manual reset required
- Simulation first: test all code in simulation before physical deployment. HIL for timing verification