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Distributed data gathering with buffer constraints and intermittent communication

We consider a team of multiple dynamical and heterogeneous robots which are deployed for gathering different types of data within a common workspace. The robots have different roles due to different capabilities: some gather data from the workspace (Type-A robots) and others receive data from Type-A robots and upload them to a data center (Type-B robots). The data-gathering tasks are specified locally to each Type-A robot as high-level Linear Temporal Logic (LTL) formulas. All robots have a limited buffer to store the data. Thus the data gathered by Type-A robots should be transferred to Type-B robots before the buffers overflow, respecting at the same time limited communication range for all robots. The main contribution of this work is a distributed task coordination and intermittent meeting scheme that guarantees the satisfaction of all local tasks while obeying the above constraints. We present numerical simulations to demonstrate the advantages of the proposed method over most existing approaches that require all-time network connectivity.