RoboMesh - Beyond 5G Distributed Ledger Technology driven Mesh for Industrial Robot Collaboration

The robotization of industry is one of the key drivers behind the Industry 4.0 revolution. Collaborative robots are becoming a reality across the manufacturing industry, autonomous robots are already a key asset in the logistics sector, and UAVs are being used for inspection and monitoring in diverse domains. Ubiquitous robots with augmented connectivity are merging into the Industrial Internet of Things, enabling higher degrees of intelligence through computational offloading. RoboMesh delves into the design and development of a framework for collaboration and long-term autonomy in distributed and heterogeneous multi-robot systems based on a Beyond-5G wireless mesh network with built-in distributed ledger technology. This framework involves data sharing, collaborative decision making, and dynamic and adaptive computational offloading, while it serves as the basis for interaction between robots and infrastructure with collaborative sensing and multi-modal sensor fusion approaches.

The multidisciplinary nature and the ambitious targets set by the project requires collaboration between partners having strong know-how and complementary expertise. RoboMesh brings together the competence and effort of the recognized experts in edge computing and distributed processing for multi-robot systems (TIERS/Univ. Turku), wireless communications (CWC/Univ. of Oulu), and advanced robotic systems and solutions (BISG/Univ. Oulu). The project also enjoys the support of the experts in the area of machine-type, automotive and industrial wireless connectivity (Prof. Roberto Verdone, Univ. Bologna), swarm robotics (Prof. Marco Dorigo, Univ. libre de Bruxelles), aerial robotics (Dr. Fabrizio Schiano, EPFL), industrial UAVs (Prof. Anibal Ollero, Univ. of Sevilla, and Antidio Viguria, CTO of FADA-CATEC), advanced robotics (Prof. Bruno Siciliano, Univ. degli Studi di Napoli Federico II), and data fusion (Prof. Eric Grange, Univ. du Québec).


[1] Jorge Peña Queralta, Li Qingqing, Eduardo Castelló Ferrer, Tomi Westerlund, "Secure Encoded Instruction Graphs for End-to-End Data Validation in Autonomous Robots", arXiv preprint (2020) . (View) (Download) (Researchgate) (arXiv)