An Approach to Reduce Communication for Multi-agent Mapping Applications

Abstract

In the context of a multi-agent system that uses a Gaussian process to estimate a spatial field of interest, we propose an approach that enables an agent to reduce the amount of data it shares with other agents. The main idea of the strategy is to rigorously assign a novelty metric to each measurement as it is collected, and only measurements that are sufficiently novel are communicated. We consider the ideal scenario where an agent can instantly share novel measurements, and we also consider the more practical scenario in which communication suffers from low bandwidth and is range-limited. For this scenario, an agent can only broadcast an informative subset of the novel measurements when the agent encounters other agents. We explore three different informative criteria for subset selection, namely entropy, mutual information, and a new criterion that reflects the value of a measurement. We apply our approach to three real-world datasets relevant to robotic mapping. The empirical findings show that an agent can reduce the amount of communicated measurements by two orders of magnitude and that the new criterion for subset selection yields superior predictive performance relative to entropy and mutual information.