Optimizing Multi-Robot Communication under Bandwidth Constraints.

Robots working collaboratively can share observations with others to improve team performance, but communication bandwidth is limited. Recognizing this, an agent must decide which observations to communicate to best serve the team. Accurately estimating the value of a single communication is expensive; finding an optimal combination of observations to put in the message is intractable. In this paper, we present OCBC, an algorithm for Optimizing Communication under Bandwidth Constraints. OCBC uses forward simulation to evaluate communications and applies a bandit-based combinatorial optimization algorithm to select what to include in a message. We evaluate OCBC’s performance in a simulated multi-robot navigation task. We show that OCBC achieves better task performance than a state-of-the-art method while communicating up to an order of magnitude less.