Explosion-Proof Paint Cobot Monitoring System with OPC UA and Beacon APIs
Automated paint cobots in hazardous environments require continuous safety telemetry monitoring to prevent thermal or atmospheric incidents. Implement a safety monitoring microservice that consumes FANUC CRX telemetry and Beacon platform events to enforce safety thresholds. Guarantee emergency shutdown triggers within 50ms of anomaly detection.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a safety-critical monitoring service that consumes FANUC cobot telemetry and Beacon platform triggers to shut down operations in Class 1 Div 1 environments.
How work is evaluated
Evaluate emergency stop command latency and correctness upon injection of thermal or gas excursion telemetry.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
latency_under_50ms
E-stop triggered within 50ms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
estop_latency_ms
Reaction latency in milliseconds • target: 20 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a streaming telemetry parsing engine for FANUC CRX-10iA/L paint hardware
Integrate Beacon platform webhook event handlers for safety trip signals
Implement emergency stop (E-stop) assertion logic with latency guarantees under 50ms
Validate safety boundary violation handling in simulated hazardous conditions
Reference links and supporting material
Telemetry stream containing internal temperature, motor torque, and gas sensor PPM values for paint cobots.
Challenge setup
Evaluate emergency stop command latency and correctness upon injection of thermal or gas excursion telemetry.
Challenge input
JSON telemetry payload with enclosure_temp_c and solvent_ppm
Python Fast-Path Runtime
Provides rapid evaluation of incoming streaming safety metrics.
Evaluated output
JSON object with trigger_estop, reaction_time_ms, and status
- E-stop triggered within 50ms
- Reaction latency in milliseconds • target: 20 • range: 0-100
- Benchmark: Robotic Safety Control Latency Benchmark
- Estop Latency Ms target: 20
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- Google ADK · Required
- Portia AI · Optional
Evaluation contract
- latency_under_50ms · Weight 1
- estop_latency_ms · Weight 1
Metrics
- estop_latency_ms · Target 20
Benchmarks
- Robotic Safety Control Latency Benchmark
- robotics
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON telemetry payload with enclosure_temp_c and solvent_ppm
Output: JSON object with trigger_estop, reaction_time_ms, and status
Execution harness: Python
Evaluation tools
- Python Fast-Path Runtime · Execution Engine
Recipe freeze marker
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