Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems

Abstract

Motivated by exploration of communicationconstrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of informationconsistency to handle situations where the plan is not initially known by all agents, and suggest an integer linear program for synthesizing information-consistent plans that also achieve auxiliary goals. Furthermore, inspired by network flow problems we propose a novel way to pose connectivity constraints that scales much better than previous methods. In the second part of the paper we apply these results in an exploration setting, and propose a clustering method that separates a large exploration problem into smaller problems that can be solved independently. We demonstrate how the resulting exploration algorithm is able to coordinate a team of ten agents to explore a large environment.

GALOPP: Multi-Agent Deep Reinforcement Learning For Persistent Monitoring With Localization Constraints

“…Although a strict connectivity constraint increases the global positional belief of the entire team, it reduces the ability of the team to persistently monitor any arbitrary region. An intermittent connectivity of agents leads to better exploration of the area allowing more flexibility [25], [12]. The auxiliary agents once disconnected do not contribute to the net rewards obtained by the team.…”