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

TL;DR

This paper introduces a new planning approach for multi-agent exploration in communication-limited environments, using information-consistency and scalable connectivity constraints to coordinate agents effectively.

Contribution

It presents a novel information-consistency concept, an efficient ILP formulation for planning, and a scalable connectivity constraint method for multi-agent exploration.

Findings

Successfully coordinated ten agents in large environments.

Scalable connectivity constraints outperform previous methods.

Effective clustering reduces problem complexity.

Abstract

Motivated by exploration of communication-constrained underground environments using robot teams, we study the problem of planning for intermittent connectivity in multi-agent systems. We propose a novel concept of information-consistency 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.