INDUSTRIAL MANTENANCE

Industrial and manufacturing facilities nationwide are suffering from staff shortages, making it difficult for them to meet their safety and security standards. While our industrial maintenance system cannot completely replace the output of a staff member, they can help free up the time and resources of current employees by completing remedial tasks that take up time and energy.

Technical Overviews

Multi-Camera Surveillance System

The multi-camera system integrates high-resolution optical and depth-sensing cameras to provide a real-time, multi-perspective view of industrial facilities. Designed for inspection surveillance, hazard testing, thermal monitoring, and leak detection, the system combines:

Two optical cameras for general observation.
Two Intel Realsense D430i depth cameras for 3D scene reconstruction and enhanced object tracking.
One high-precision optical camera on the robotic arm for close-up inspections of systems and machinery.
One Thermal camera for calibration testing (optional).

The system processes live video feeds using a distributed edge computing architecture, where:

High-bandwidth video streams are compressed and transmitted over the network for remote monitoring.
Onboard AI models (YOLO-based object detection) process real-time frames to detect anomalies.
SLAM-based scene mapping integrates depth data for precise spatial awareness and automated navigation adjustments.

The video analytics backend leverages GStreamer pipelines for efficient processing and supports H.265/H.264 encoding for storage and transmission. Video data is indexed with timestamped metadata, allowing forensic analysis and playback during incident reviews.

Network and Control Interface:

Supports low-latency RTSP streaming for real-time monitoring on the web-based control dashboard.
Offers on-demand playback and event tagging for safety auditing.
Configurable edge-to-cloud video archiving (optional) for long-term evidence retention.

This modular surveillance system improves situational awareness for foremen, reduces blind spots, and enhances safety by delivering AI-assisted monitoring with minimal manual intervention.

Two-Way Audio System

The two-way audio communication system facilitates secure and real-time voice interaction between workers. It integrates:

A high-fidelity, noise-canceling microphone array, capable of isolating speech from background noise.
A directional speaker system, optimized for clarity even in acoustically challenging environments like concrete-walled corridors.
Dynamic voice modulation, adjusting playback volume based on ambient noise levels.

The system supports:

Live voice relay via the control interface.
Automated alerts, where the AI assistant can convert detected events into spoken warnings (e.g., “leak detected”).
Push-to-talk and always-on modes, configurable through the web dashboard.

For security, all audio transmissions are encrypted using TLS-secured WebRTC channels, preventing eavesdropping or unauthorized access.

This system enhances command efficiency and response coordination, allowing workers to communicate seamlessly with each other without direct engagement.

Autonomous Navigation and Obstacle Avoidance

The autonomous navigation system enables the robot to patrol indoor and outdoor facilities without human intervention. The system is built on a SLAM (Simultaneous Localization and Mapping) framework, which combines:

Lidar-based and vision-based mapping to create an accurate environmental model.
Real-time obstacle detection using depth cameras and AI-assisted scene interpretation.
Dynamic path planning, allowing the robot to adjust its route in response to moving objects and unexpected obstacles.

The system operates in two primary modes:

Autonomous patrols, where the robot follows predefined routes and dynamically adjusts based on environmental feedback.
Manual control, allowing workers to override navigation via a web-based interface or handheld controller.

Positioning accuracy is enhanced through sensor fusion, integrating:

IMU (Inertial Measurement Unit) data for precise motion tracking.
Leg odometry corrections, improving stability on rough surfaces.
GPS localization (optional for open-yard operations).

This autonomous system reduces the workload of staff, increases patrol coverage, and enhances safety enforcement with continuous monitoring.

6-Searchlight System (Included with SHEP-LEO2 Models)

The object detection system is built on YOLO (You Only Look Once) deep learning models, trained specifically for industrial facility environments. It enables:

Real-time object detection, identifying fire extinguishers, open/closed doors or windows, etc.
Automated anomaly recognition, flagging anomalies and sending a report through the proper channels.

The detection pipeline is optimized using:

TensorRT acceleration for low-latency inference.
Model retraining with prison-specific datasets, ensuring high detection accuracy.
Threshold-based alerting, triggering officer notifications based on confidence scores.

Detected objects are logged with timestamps, confidence levels, and associated patrol locations, creating an auditable security record.

Robotic Arm Inspection System

The robotic arm is an advanced inspection tool equipped with:

6 degrees of freedom (DoF) articulation, enabling precise manipulation.
An integrated optical camera, delivering detailed close-up visual analysis.
A precision gripper, capable of handling small objects, doors, and secured enclosures.