Automation Workflow for Autonomous Ground Robot (UGV) Patrol and Inspection

Replaces 2-3 daily personnel patrols of perimeter fencing, below-grade excavations, and overnight site security with autonomous UGV routes. This directly reduces labor costs and, more critically, removes personnel from potential fall hazards, confined spaces, and after-hours security incidents. The workflow includes geofenced navigation, live video feed to security hubs, and automated incident logging.

UGVs equipped with thermal, gas, and visual sensors can patrol inaccessible or unsafe zones (e.g., deep trenches, unstable slopes) continuously, providing data feeds that were previously sporadic or impossible to collect. This 24/7 coverage improves early detection of issues like water intrusion, unauthorized access, or equipment tampering, preventing costly delays or theft.

When a UGV detects an anomaly (e.g., a breached fence, a gas leak, an active fire), the workflow automatically triggers alerts with precise GPS coordinates, sensor readings, and visual evidence to the site superintendent and security team. This cuts incident verification and response time from 30+ minutes to under 5 minutes, limiting potential damage and creating an auditable, timestamped record for insurance and compliance.

Demonstrating proactive risk management through automated patrols and continuous hazard monitoring is a tangible input for underwriters. A documented reduction in high-risk human activities and faster incident response can lead to lower general liability and workers' compensation premiums. The defensible log of patrols and conditions also strengthens the GC's position in any post-incident litigation.

Beyond basic patrols, the workflow can be configured to verify that secured materials are in place, that temporary works (scaffolding, shoring) are intact, and that authorized personnel are in designated zones after hours. This automated oversight reduces reliance on manual checks, deters internal theft, and ensures subcontractor compliance with site safety and security protocols without adding supervisory headcount.

The structured, time-series data collected by UGVs (thermal patterns, vibration, environmental conditions) becomes a feed for predictive models. Over time, this enables the transition from reactive patrols to predictive alerts—for example, flagging areas of soil erosion risk based on moisture trends or predicting equipment failure from anomalous thermal signatures, preventing issues before they impact schedule or budget.

The UGV's core autonomy stack ingests geofenced site maps and 4D BIM coordinates to generate optimal patrol routes. Using SLAM and real-time LiDAR/vision for obstacle avoidance, it dynamically replans around unexpected site debris or closed pathways. This replaces manual remote driving, enabling 24/7 operation in low-light or hazardous zones like excavations and freeing security personnel for higher-value tasks.

Modular sensor pods (thermal imaging, gas detectors, high-resolution cameras, acoustic monitors) are orchestrated based on patrol intent. A central agent triggers sensor activation—e.g., thermal scans for unauthorized personnel at night, methane detection in below-grade areas. Sensor fusion logic correlates anomalies across modalities to reduce false positives before escalating.

To maintain low latency off-grid, lightweight vision models run on the UGV's edge compute unit, classifying objects (person, vehicle, equipment) and detecting state changes (open gate, water pooling). Only compressed anomaly clips and metadata are transmitted via cellular or mesh network to the central orchestration layer, minimizing bandwidth costs and preserving operational continuity if connectivity drops.

A central agent (built with frameworks like LangGraph) ingests UGV alerts, scores severity using predefined rules (e.g., 'thermal signature in restricted zone' = HIGH), and routes them to the appropriate human or system. High-severity alerts trigger immediate SMS to the site superintendent; equipment idling alerts create a work order in the CMMS (e.g., ServiceNow); all events log to the project's digital twin for audit.

The workflow automatically generates inspection reports for safety and compliance (e.g., perimeter integrity logs, environmental monitoring data). All patrol data, including anomaly geotags and resolved alerts, is pushed to the project's digital twin platform (e.g., Autodesk BIM 360, Bentley iTwin), creating a living as-built record that compares physical site state against the planned model for progress and deviation tracking.

Pilot deployment begins with a single UGV on a defined, low-risk night patrol loop for 2-3 weeks. The build phase integrates with existing site systems: mapping to the project BIM, configuring alert APIs to the superintendent's communication tools, and setting up charging station logistics. Full rollout scales to multiple UGVs with staggered patrol schedules, managed via a unified fleet dashboard that shows battery status, alerts, and coverage heatmaps.