Multi-Robot Rendezvous in Unknown Environment with Limited Communication

TL;DR

This paper presents a novel multi-robot rendezvous approach in unknown environments that leverages limited communication for environment exploration, topological mapping, and efficient rendezvous point selection, validated through simulations and real-world tests.

Contribution

It introduces PIER, a partitioned exploration strategy with lightweight topological maps, and a rendezvous point selection algorithm, advancing multi-robot rendezvous in unknown environments with limited communication.

Findings

PIER enables efficient environment exploration with minimal communication.

The rendezvous point selection algorithm improves convergence efficiency.

Validated effectiveness in both simulations and real-world experiments.

Abstract

Rendezvous aims at gathering all robots at a specific location, which is an important collaborative behavior for multi-robot systems. However, in an unknown environment, it is challenging to achieve rendezvous. Previous researches mainly focus on special scenarios where communication is not allowed and each robot executes a random searching strategy, which is highly time-consuming, especially in large-scale environments. In this work, we focus on rendezvous in unknown environments where communication is available. We divide this task into two steps: rendezvous based environment exploration with relative pose (RP) estimation and rendezvous point selection. A new strategy called partitioned and incomplete exploration for rendezvous (PIER) is proposed to efficiently explore the unknown environment, where lightweight topological maps are constructed and shared among robots for RP estimation with very few communications. Then, a rendezvous point selection algorithm based on the merged topological map is proposed for efficient rendezvous for multi-robot systems. The effectiveness of the proposed methods is validated in both simulations and real-world experiments.