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Deployment of Autonomous Robotics for Metro Station Sanitation and Maintenance Monitoring

osedea.com

Government

Utilities

Environmental Services

Operational Challenges in Manual Metro Station Maintenance

STM faces significant inefficiencies in manual sanitation and maintenance inspections, including time constraints (limited to 1am-4am window), inconsistent anomaly detection (trash, graffiti, burnt lightbulbs), infrastructure limitations (non-automated doors/turnstiles), and unstructured data management from high-volume image capture. Current processes require significant human resources without systematic trend analysis capabilities.

About the Client

Public transportation authority operating metro, bus, and paratransit services, seeking technological innovation for operational efficiency and customer experience improvement

Key Goals for Autonomous Inspection System

Automate nightly station inspections using autonomous robotics
Improve anomaly detection accuracy to 85%+ through AI-powered image analysis
Reduce manual labor requirements by 50% for routine inspections
Create structured data repository for historical trend analysis
Enable real-time dispatch of maintenance crews based on detected anomalies

Core System Capabilities

Autonomous navigation through complex public spaces without infrastructure modifications
Multi-angle 360-degree imaging system with thermal and visual spectrum capture
Real-time object detection using YOLOv5 model with custom anomaly classification
Centralized dashboard for data visualization and technician feedback
Automated alert system for maintenance dispatch
Historical data analysis tools for trend identification

Target Technology Stack

Boston Dynamics Spot Explorer platform

Spot CAM+IR imaging system

Spot CORE onboard computing module

YOLOv5 computer vision model

ROS (Robot Operating System)

Cloud-based data storage with edge computing capabilities

System Integration Needs

Existing STM maintenance dispatch systems
IoT sensor networks for environmental monitoring
Public safety and security systems
Workforce management platforms

Critical System Attributes

99.9% uptime during operational hours
Data processing latency under 2 seconds
IP67 dust/water resistance rating for robotic platform
End-to-end encryption for data transmission
Scalable architecture supporting 100+ robotic units

Anticipated Business Outcomes

Implementation of autonomous inspection systems is projected to reduce manual inspection costs by 40-60%, improve anomaly detection coverage to 95% of station areas, and enable predictive maintenance scheduling through historical data analysis. The system will free staff for higher-value tasks while providing actionable insights for optimizing cleaning schedules and resource allocation based on verified usage patterns.