CircuBot – AI-Powered Modular Collaborative Robotic System for Object Sorting
SHORT SUMMARY
CircuBot is a modular collaborative robotics TestBed designed to support experimentation, validation, and deployment of advanced waste-sorting technologies. It integrates collaborative robots, AI-driven computer vision, soft adaptive gripping, and cloud-based data analytics to enable safe, flexible, and efficient separation of recyclable materials such as PET bottles, metal cans, and small electronic components.
The TestBed provides a controlled yet realistic industrial environment for testing circular economy solutions under real-world conditions, supporting Technology Readiness Levels (TRL) 3–8. CircuBot enables rapid Proof-of-Concept validation and performance assessment for DeepTech innovations in robotics and artificial intelligence.
Aligned with Industry 5.0 principles, CircuBot emphasizes human-centric automation, environmental sustainability, and resilient manufacturing by improving worker safety, enhancing material recovery, and enabling data-driven process optimization. Within the CITADELS framework, the TestBed offers accessible infrastructure for research organizations, SMEs, and industry stakeholders to develop and validate intelligent, sustainable automation solutions.
HOSTING INSTITUTION AND PI INFO
| Name of Host Organization | University of Belgrade – School of Electrical Engineering (ETF) |
| Department or Lab | Department of Signals and Systems, ETF Robotics lab |
| Name of Building | Palace of Science |
| Physical Address | Kralja Milana 11, 11000 Beograd, Serbia |
| Website Links | https://circubot.etf.rs |
| Institutional contact name | Prof. dr Kosta Jovanović |
| Institutional contact email | kostaj@etf.rs |
APPLICATION CASES
| Application case: | Short description: |
| Automated industrial sorting (robot control & manipulation) | Fully autonomous robotic sorting of waste on a conveyor belt. AI-based vision performs real-time object detection and classification, while motion planning generates efficient pick-and-place or pick-and-toss actions using a soft robotic gripper adaptable to irregular shapes. |
| Collaborative human–robot sorting (real-time object detection & task allocation) | Shared human–robot workspace enabling safe collaboration during sorting tasks. Real-time perception and safety monitoring support dynamic task allocation, with robots handling repetitive items and humans managing complex or uncertain objects. |
| Sorting strategy design and optimization | Experimental evaluation and optimization of sorting strategies, including conveyor speed, grasping methods, and robotic cell layouts, using performance metrics such as throughput, accuracy, purity rate, and cycle time. |
| AI model training, validation, and benchmarking | Training and benchmarking of machine-learning models for waste classification under realistic conditions, including variable lighting, occlusions, cluttered scenes, and mixed-material streams. |
| Education, research, and innovation support | Use of the CircuBot testbed for student projects, academic research, professional training, and proof-of-concept demonstrations for SMEs and startups, supporting technology transfer and skill development. |
POTENTIAL STAKEHOLDERS
| Non-academic stakeholders | |
| Industrial Partners | Pilot deployment and validation studies of robotic sorting, handling, and collaborative operation scenarios under real industrial conditions. |
| SMEs | Testing and prototyping of robotic subsystems, perception algorithms, and circular-economy solutions with reduced development cost and risk. |
| Startups | Rapid validation of innovative robotics and AI concepts related to waste sorting, automation, and sustainability, supporting product development and market readiness. |
| Government Bodies | Evaluation of technological solutions supporting waste management strategies |
| Professional Associations | Dissemination of best practices, technical guidelines, and standards related to collaborative robotics and sustainable waste management. |
| Community | Benefiting from improved recycling infrastructure, increased efficiency of waste processing, and reduced landfill waste, contributing to environmental sustainability. |
| Others 1 (comma-separated) | Environmental NGOs, Waste management authorities, Technology transfer offices |
| Academic stakeholders | |
| Undergraduate students | Hands-on training in robotics, computer vision, and automation through laboratory exercises, project-based learning, and introductory research activities. |
| MSc students | Development and validation of advanced algorithms and system components within master theses focused on robotics, AI, and circular economy applications. |
| PhD students | Long-term experimental research on collaborative robotics, perception, control, and human–robot interaction. |
| Researchers | Experimental validation of scientific hypotheses, development of new methods, publication of research results, and participation in national and international research projects. |
| Others 2 (comma-separated) | Visiting researchers, Postdoctoral fellows, Academic collaborators |
| Other types of stakeholders | |
| Others 3 (comma-separated) | European research partners, Standardization bodies, Funding agencies |
