Stream-based Real-time Monitoring of Multi-Robot Systems

A major challenge in monitoring multi-robot systems is the aggregation of high-rate data sets before sending them over low-rate data links. Processing data locally requirers intensive tuning and calibration in order to avoid exceeding the memory limitations on individual robots while, at the same time, minimizing the impact on the available network communication bandwidth. We present RTLOLA, a monitoring framework for multi-robot systems with built-in guarantees on memory and bandwidth consumption. RTLOLA computes the memory required for each aggregation step and thereby determines how much of the data aggregation can be carried out on a robot without exceeding its memory. By aggregating the data as much as possible already on the robot, the impact of each robot on the available bandwidth of the network is minimized. As a result, the number of robots that can be supported by a given network is maximized. As a case study, we apply RTLOLA to the monitoring of a fleet of autonomous robot taxis based on the Uber transportation mediation service.

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