Intermittent Connectivity for Exploration in Communication-Constrained Multi-Agent Systems
This addresses exploration challenges for robot teams in underground or constrained settings, with incremental improvements in scalability and coordination.
The paper tackles the problem of planning for intermittent connectivity in multi-agent systems for exploring communication-constrained environments, proposing a novel information-consistency concept and scalable connectivity constraints, and demonstrates coordination of ten agents to explore a large environment.
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.