Topic 6: Industrial Deployment Simulation & Field Test

Topic 6 integrates everything from the course into a digital twin stress test and a scoped field trial. You will learn how to structure experiments, define entry and exit criteria, and use metrics to evaluate whether your robot or fleet is truly ready for real-world deployment.

6.1 Module A — Warehouse Digital Twin Stress Test

Building a Realistic Digital Twin

You will:

• Extend your simulation worlds (from Chapter 3) into warehouse- or lab-style digital twins that include:
  • Aisles, shelves, and work cells.
  • Docks, charging stations, and staging areas.
  • Human traffic and other robots.
• Represent:
  • Key workflows (e.g., pick/pack, inspection routes, transfers).
  • Known bottlenecks (e.g., narrow aisles, busy intersections).

Multi-Robot Load Testing

The goal of load testing is to explore peak and failure conditions, not just nominal behavior:

• Many simultaneous jobs.
• High-density robot traffic.
• Frequent interactions with simulated humans or moving obstacles.

You will:

• Script scenarios where:
  • All robots start tasks at once.
  • Orders arrive in bursts.
  • Certain resources (e.g., docks, lifts) become temporarily unavailable.
• Observe:
  • Queue lengths.
  • Route conflicts and congestion.
  • How well your scheduling and routing policies adapt.

Edge Cases and Failure Injection

You will deliberately inject:

• Blocked shelves and mis-labeled or missing items.
• Simulated sensor outages (cameras or LiDAR disabled).
• Artificial network delays or drops between robots and coordinators.

For each injected fault, you will:

• Define expected robot behavior (e.g., pause, reroute, ask for help).
• Check logs and dashboards to ensure the event is:
  • Detected.
  • Properly recorded.
  • Visible to operators.

6.2 Module B — Live Deployment Field Trial

Designing a Scoped Field Trial

Field trials must be:

• Carefully scoped.
• Supervised.
• Reversible.

You will:

• Define:
  • A limited environment (e.g., one aisle, one lab room).
  • A subset of tasks (e.g., a small pick list, a simple inspection route).
  • The presence of trained operators or safety officers.
• Specify:
  • Entry criteria (e.g., simulation tests passed, safety checks complete).
  • Exit criteria (e.g., maximum number of faults, time limit, or success threshold).

Risk Assessment and Rollback Plans

Before running the trial, you will:

• Conduct a structured risk assessment:
  • Identify hazards.
  • Estimate severity and likelihood.
  • Document mitigations.
• Design rollback plans:
  • Conditions under which the trial must be stopped.
  • Steps to return robots and environment to a safe baseline.

Exercising Recovery Pathways

The trial is also an opportunity to validate recovery behaviors from Topic 5:

• Intentionally trigger:
  • E-stop.
  • Low-battery docking.
  • Simulated network loss (where safe).
• Confirm that:
  • Robots follow expected safe behaviors.
  • Logs clearly indicate what happened and when.
  • Operators can follow runbooks to bring systems back online.

6.3 Module C — Performance Metrics & Uptime Analytics

Task and Fleet-Level Metrics

To evaluate readiness, you need clear metrics:

• Task-level:
  • Task success and failure rates.
  • Rework rates (tasks that had to be retried).
  • Average and tail (e.g., 95th/99th-percentile) completion times.
• Fleet-level:
  • Jobs per hour/day.
  • Utilization per robot.
  • Distribution of idle vs active vs faulted time.

You will:

• Design data schemas and dashboards that present these metrics to operators and engineers.

Reliability and Availability Metrics

Industrial systems often track:

• MTBF (Mean Time Between Failures) — how often a fault that requires intervention occurs.
• MTTR (Mean Time To Repair) — how long it takes to restore service.
• Availability over time (e.g., percentage of scheduled time when the system is able to perform tasks).

You will:

• Learn how to approximate and interpret these metrics from logs.
• Use them to:
  • Compare different hardware or software configurations.
  • Identify modules that are disproportionately responsible for downtime.

From Trial to Continuous Improvement

Finally, Topic 6 emphasizes that deployment is not an end state:

• Each trial produces data that should feed back into:
  • Simulation model updates.
  • Safety and maintenance procedures.
  • Scheduling and workflow changes.

You will:

• Outline a continuous improvement loop:
  • Plan → Test (sim + field) → Measure → Analyze → Improve.
• Connect this loop to your capstone:
  • Document what you would change in future iterations.
  • Identify which metrics you would watch most closely in a real deployment.