IEEE TSYP 12 RAS PES SIGHT challenge

This project presents an innovative, eco-friendly solution for autonomous waste collection using a coordinated system of drones and ground robots. Powered by solar energy, the system integrates computer vision, machine learning, and SLAM navigation to detect, classify, and collect waste with minimal human intervention. The drone identifies trash locations and sends coordinates to robots via cloud infrastructure, enabling efficient and safe collection and sorting. Advanced AI models like Efficient Diet and YOLOv4-tiny are used for accurate detection and classification, while a hybrid genetic algorithm optimizes the robots? routing. The system significantly improves waste collection efficiency by automating the process with minimal human intervention, leading to reduced labor costs and faster waste retrieval. The project engages local communities by introducing eco-friendly solutions to waste management, encouraging public awareness about environmental sustainability. The project faced challenges related to real-time data processing, waste detection accuracy in complex environments.These were addressed through the integration of machine learning algorithms to improve the accuracy of waste detection. The system is designed to be scalable, with the ability to adapt to various environments, from urban areas to industrial zones. The use of solar power provides long-term energy sustainability.