Generative AI-Driven High-Speed Robotic Recycling
Waste management faces significant challenges with traditional sorting methods being labor-intensive, inefficient, and prone to error. These manual processes expose workers to hazardous materials, reduce sorting speed, and lower recycling rates, leading to environmental pollution and increased landfill use. To tackle these issues, we are developing Generative AI driven High-Speed Robotic Recycling. This system integrates advanced robotics, AI, and machine vision to automate and improve waste sorting, enhancing efficiency, accuracy, and safety. Delta robots and custom-built arms handle the sorting, while AI, including deep learning and reinforcement learning, optimizes detection and decision-making. High-resolution cameras and AI-driven image processing ensure accurate waste categorization. Inspired by successful projects like RecyBot, a high-speed recycling robot developed by Prof. Dzmitry Tsetserukou (the co-owner of the present practice) and MIT, our solution aims to lead in sustainable waste management across BRICS nations. By automating the process, we reduce labor risks, increase sorting speed and accuracy, and significantly boost recycling rates, contributing to environmental sustainability.
Climate And Environmental Technologies
Technologies for the healthy environment
The initial prototype of the system, currently under development, is expected to be operational within the next 12 months. Following this, the industrial-grade version, scalable to other BRICS countries, will be established within the subsequent 12 months.
The Generative AI driven High-Speed Robotic Recycling system will first be implemented in Tehran, Iran, and Moscow, Russia. Tehran is chosen for its significant waste management challenges and the involvement of local experts. Moscow is selected due to its advanced infrastructure and collaboration with Skoltech. After successful deployment in these cities, the project will expand to other BRICS nations, targeting key urban centers in Brazil, India, China, and South Africa. These locations are selected for their urban challenges, technological advancements, and commitment to sustainable waste management. The goal is to promote sustainable practices across the BRICS alliance.
Generative AI-Driven High-Speed Robotic Recycling offers several key advantages over traditional and automated waste sorting methods: Efficiency and Accuracy: Advanced AI like YOLO enables precise real-time sorting, significantly improving accuracy. Delta robots process waste rapidly, boosting overall efficiency. Cost-Effectiveness: Automation cuts labor costs and optimizes resource use, maximizing material recovery. Scalability: The modular design allows easy scalability and customization for different waste types and environments. Safety: Automation reduces human exposure to hazardous materials, enhancing safety and working conditions. Environmental Impact: High accuracy increases recycling rates and reduces landfill waste, promoting sustainability. Technological Innovation: Cutting-edge AI and robotics keep the system at the forefront of waste management technology. Market Positioning: Unique integration of AI, robotics, and machine vision, supported by strong academic partnerships, differentiates the project in the industry. This project aims to transform waste management with a scalable, cost-effective, and sustainable solution for diverse stakeholders.
Professor Dzmitry Tsetserukou has received numerous awards, including: Multiple Excellence Recognition Awards from Skoltech. Recognition as being in the Top 2% of World’s Top Cited Researchers. Best Paper Awards at prominent conferences like Asia Haptics and IEEE SMC. Best Career Trainer of The Year Award from Skoltech. Outstanding Reviewer at IEEE Haptics Symposium. Best Demonstration Awards at ACM SIGGRAPH Asia and other conferences. Vice-champions of Eurobot World. Several prestigious fellowships, including those from the Japanese Government and Japan Society for the Promotion of Science.
Professor Dzmitry Tsetserukou has authored several key papers, including: Pose Estimation and Charging Robots research in top AI and robotics journals (2024, 2021). WareVision for warehouse automation using UAVs (2020). Bi-VLA and GrainGrasp models for advanced robotic manipulation (2024). Multi-view 3D Reconstruction dataset presented at CVPR (2023). Studies on DeepScanner and MobileCharger for robotic systems (2021). Work on Haptics for robotic interaction and smart factories (2018, 2019).
