Swarm exploration and Communications: Integration by probabilistic learning (SCIL)

• Applicants: Professor Dr.-Ing. Armin Dekorsy; Dr. Dmitriy Shutin
• Subject Area: Communication Technology and Networks, High-Frequency Technology and Photonic Systems, Signal Processing and Machine Learning for Information Technology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 500260669

Distributed Artificial Intelligence (DAI) is the part of AI that deals withthe concurrency of AI computations, i.e. with problems of distributionand coordination of knowledge and action in multi-agent systems.Exploration problems involving multiple agents – swarm exploration –exemplify well DAI. Swarm exploration is concerned with distributedprocessing within the swarm of sensed data for reconstructing anunknown physical, chemical or other process of interest. Itincorporates methods for distributed sensing, optimized (intelligent)information gathering and agent movement/action coordination.Communication is therefore always an integral part of a swarmexploration. Swarm exploration often considers reliable and error-freecommunications. However, communication systems do adduncertainty to exchanged information. For instance, communicationuncertainty needs to be taken into account when predicting newsampling positions for agents as locations causing severecommunication degradation will be useless for distributed informationprocessing/exploration purposes. Likewise, communication systemsare designed to aim for error-free transmission of measurements orprocessing results, but they are neither aware of their relevance forlearning the entire explored process nor of the confidence in the datato be transmitted. The main goal of this research project is thedevelopment of a framework and methodology as well as the design,investigation and proof-of-concept demonstration of algorithms of a“tight” integration of exploration and communication. To this end, wemake exploration “communication-aware” and communications“exploration-aware” by using tools of probabilistic learning, thusenabling the coordination of knowledge and action in multi-agentnetworks in the sense of DAI. With this in mind, the researchproposed in this work addresses challenges in development of futuredistributed sensing and data processing platforms – sensor networksor mobile robotic swarms consisting of multiple agents – that cancollect, communicate, and process spatially distributed sensor data.

• DFG Programme: Research Grants