Project: Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems (DEEPCOBOT)
Period: 2020 – 2025
About the project
The Mechatronics Centre and WISENET Centre and at UiA have joined forces in a new multidisciplinary frontier research project that combines Artificial Intelligence and Robotics. They have received NOK 16 million (total budget 20 NOK million) from the Research Council of Norway to develop the next-generation artificial intelligence for industrial collaborative robots. The project is called Collective Efficient Deep Learning and Networked Control for Multiple Collaborative Robot Systems (DEEPCOBOT). The project period for DEEPCOBOT is from 2020 to 2025. The plan is for the project to have three PhD candidates and one postdoctoral fellow.
Project objectives and Vision
In recent years, collaborative robot systems have received a great deal of attention in the context of a large number of industrial applications, such as additive manufacturing, automotive manufacturing, material handling, packaging and co-packing and quality inspection. The collaboration between multiple collaborative robots (Cobots) and human operators is considered to be the most prominent strategy in Industry 4.0 and future Industry 5.0, sharing the same space and collaborating on tasks according to their complementary capabilities.
DEEPCOBOT project will investigate the design of a new generation of decentralized data-driven deep learning based controllers for multiple coexisting Cobots, which interact both between themselves and with human operators in order to collectively learn from each other’s experiences and perform cooperatively different complex tasks in large-scale industrial environments.
The vision of this project is that the learning of the optimal local control policies can be substantially accelerated by sharing both information about previous experiences and computation across multiple neighbor robots connected through a wireless communication network, providing solutions that satisfy the necessary real-time constraints in the considered robotic applications, as well as providing sufficient robustness and interchangeability to the control solutions. This multidisciplinary project covers the areas of deep learning, advanced control, optimization, reinforcement learning, decentralized shared control, embodied artificial intelligence, bi-directional interaction between robots and human operators, and cross-layer networking with a significant potential in industrial applications.
Funding
The project is funded by the Norwegian Research Council’s ICT and digital innovation program, IKTPLUSS, within the section of Transformative Research. The Program is called “Ubiquitous Data and Services – Researcher Project”. The Program funds long-term projects that generate new knowledge and technology that promote productivity and efficiency.
Partners
The project will involve a collaboration between UiA and the partners Mechatronics Innovation Lab (MIL), Omron Electronics Norway, ABB Norway, the University of California San Diego (USA), KTH Royal Institute of Technology (SWE) and the University of Navarra (SPA).
Publications
Book
- J. Zhou, L. Xing, C. Wen, “Adaptive Control of Dynamic Systems with Uncertainty and Quantization”, Taylor & Francis, CRC Press, 2021
Scientific Publications
2022
- J. Singh, J. Zhou, B, Beferull-Lozano, I. Tyapin, “Learning Cooperative Multi-Agent Policies with Multi-Channel Reward Curriculum Based Q-Learning”. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON), 2022
- S.M. Schlanbusch, O. M. Aamo, J. Zhou,“Attitude Control of a 2-DOF Helicopter System with Input Quantization and Delay”. Proceedings of the Annual Conference of the IEEE Industrial Electronics Society (IECON), 2022
- J. Zhou, S.M Schlanbusch, Adaptive Quantized Control of Offshore Underactuated Cranes with Uncertainty, IEEE 17th International Conference on Control & Automation (ICCA), 2022, pp. 297-302
2021
- S.M. Schlanbusch, J. Zhou, R. Schlanbusch, “Adaptive attitude control of a rigid body with input and output quantization”, IEEE Transactions on Industrial Electronics, vol. 69 (8), pp. 8296-8305, 2021
- S.M. Schlanbusch, J. Zhou, R. Schlanbusch, “Adaptive backstepping attitude control of a rigid body with state quantization”, 60th IEEE Conference on Decision and Control (CDC), 2021, pp. 372-377
- S.M. Schlanbusch, J. Zhou, “Adaptive Backstepping Control of a 2-DOF Helicopter System in the Presence of Quantization”, IEEE The 9th International Conference on Control, Mechatronics and Automation (ICCMA), 2021