جامعة المعقل

CCBot is an autonomous tracked inspection and manipulation robot developed for industrial environments where human access is hazardous or impractical. The platform combines autonomous navigation, robotic manipulation, edge artificial intelligence, and real-time monitoring into a single integrated system capable of performing inspection, hazard detection, and remote intervention tasks. Key Features • Autonomous tracked navigation with GPS waypoint missions and intelligent obstacle avoidance. • 5-DOF robotic arm with eye-in-hand vision for remote manipulation, equipment interaction, and object handling. • Real-time edge AI using a YOLO-based vision system for PPE compliance monitoring and hazard detection. • Automatic image capture with GPS-tagged inspection logging for complete mission documentation. • Browser-based Ground Control Station providing live video, telemetry, mission planning, robotic arm control, and real-time system monitoring. • AI-powered engineering report generation that automatically converts inspection data, sensor readings, and captured evidence into professional reports. • Multi-layer safety architecture featuring watchdog monitoring, command-loss failsafe, obstacle protection, and autonomous emergency stop. • Modular hardware and software architecture supporting future integration of thermal imaging, gas sensors, LiDAR, radiation monitoring, and advanced autonomous capabilities. Applications • Industrial facility inspection • PPE compliance monitoring • Environmental and safety monitoring • Remote equipment operation • Hazardous area exploration • Warehouse and plant automation Key Innovation CCBot integrates autonomous mobility, robotic manipulation, real-time computer vision, environmental sensing, and AI-generated engineering reports into a single intelligent platform, delivering a complete end-to-end solution for modern industrial inspection while improving worker safety, inspection efficiency, and operational reliability.

It is a wearable Internet of Things (IoT) device designed to protect the elderly, based on a microcontroller (ESP32) and a Gyroscope sensor. The system features local data processing via a built-in artificial intelligence model (Gradient Boosting) that achieves 99.16% accuracy, completely eliminating false alarms without the need for cloud servers. When a real fall is detected, the system offers an alternative and cost-effective solution to GPS by using signals from surrounding Wi-Fi networks to pinpoint the patient’s location with high accuracy, even inside closed rooms, and immediately sends an automated distress call via Telegram that includes a Google Maps link to rescue the patient as quickly as possible.