Topic 5: Resilience, Failover & Self-Recovery Systems

Topic 5 treats your robot or fleet as a fault-tolerant system. Instead of assuming that hardware, networks, and software will always work, you will design explicit mechanisms to detect failures, contain their impact, and recover to a safe, operational state whenever possible.

5.1 Failure Detection Patterns

Motor Stall and Anomaly Detection

Motor stalls and mechanical issues are common field failures:

• An arm encounters an unexpected obstacle.
• A wheel or leg becomes jammed by debris.
• A gearbox begins to seize due to wear or contamination.

You will:

• Identify signals that indicate possible stalls:
  • Elevated current at near-zero velocity.
  • Divergence between commanded and measured positions.
  • Repeated overcurrent or thermal warnings.
• Design responses:
  • Immediate stop or back-off maneuvers.
  • Logging of the event.
  • Escalation to operators when repeated in the same joint or area.

Low-Battery Prediction and Alerts

Sudden power loss can cause:

• Unsafe stops.
• Lost logs and incomplete tasks.

You will:

• Use battery models and telemetry (voltage, current, consumed charge) to estimate:
  • Remaining runtime.
  • Distance that can still be traveled.
• Design alert thresholds:
  • Early warnings to scheduling systems.
  • Hard cutoffs for initiating safe shutdown or docking.

Vision Degradation and Sensor Dropout

Degraded perception can be as dangerous as actuator faults:

• Cameras obscured by dust or smears.
• LiDAR partially blocked or misaligned.
• Sensors going offline due to cables or power issues.

You will:

• Implement health checks:
  • Monitoring image brightness, contrast, and histogram statistics.
  • Tracking LiDAR return rates or range distributions.
  • Detecting missing or delayed topics and TF frames.
• Define mitigation strategies:
  • Slowing or stopping motion when key perception channels are compromised.
  • Switching to backup sensors when available.

5.2 Shadow Controllers and Redundancy

Backup Controllers and Fallback Modes

High-level planners or perception pipelines may:

• Hang due to unforeseen bugs.
• Crash under unusual input.
• Become unresponsive when compute is overloaded.

You will:

• Design shadow controllers:
  • Simplified controllers that can maintain basic stability and safety (e.g., keep the robot balanced, hold position, or slowly come to a stop).
  • Watchdog processes that monitor primary controllers and trigger handover if timeouts occur.

Redundant Sensors and Compute

For safety-critical functions:

• Single points of failure are risky.

You will:

• Explore redundancy strategies:
  • Dual IMUs or redundant encoders on key joints.
  • Separate compute paths for safety-critical logic vs non-critical tasks.
  • Independent communication paths where possible.

Network Partition Tolerance

Robots may lose:

• Connectivity to cloud services.
• Links to centralized fleet managers or dashboards.

You will:

• Design a local autonomy and safety baseline:
  • Behavior when central coordination is unavailable (e.g., finish current task, then return to a safe zone).
  • Local E-stop and safety loops that function without any network.

5.3 Safe Recovery & Graceful Shutdown

Autonomous Fall Recovery and Safe Postures

Falls or near-falls are critical events for humanoids:

• Risk of damage to hardware.
• Risk to nearby humans.

You will:

• Develop concepts for:
  • Detecting falls via IMU and joint state anomalies.
  • Transitioning to safe postures (e.g., kneeling, sitting) that minimize further damage.
  • Conditions under which automatic recovery is allowed vs when human inspection is mandatory.

Task Suspension and Resume Capability

Operationally:

• Interruptions will happen (E-stops, low battery, blocked paths).

You will:

• Design task representations that support:
  • Pausing execution and recording progress.
  • Resuming when conditions are safe again.
  • Re-issuing or rerouting tasks that were aborted mid-way.

Self-Docking and Charging Behaviors

Charging behavior is a recurring resilience pattern:

• Robots should not run to zero battery.
• Docking must be robust to minor perception errors and obstacles near docks.

You will:

• Define:
  • Triggers for initiating docking (battery thresholds, idle windows).
  • Docking procedures with multiple approach and retry behaviors.
  • Fallbacks when docks are blocked or offline.

Topic 5 closes by encouraging you to think of your system as always partially failing somewhere, and to design so that these failures are contained, visible, and recoverable rather than catastrophic.