Multi-AGV's Temporal Memory-based RRT Exploration in Unknown Environment

TL;DR

This paper introduces TM-RRT, a novel multi-robot exploration strategy that uses temporal memory to reduce overlap and improve efficiency in unknown environments, outperforming conventional RRT methods.

Contribution

The paper presents a temporal memory-based RRT (TM-RRT) approach that adaptively assigns frontiers and shares memory among robots to enhance exploration robustness.

Findings

Successfully explored a 1350 m² area in simulations and real deployment.

TM-RRT outperformed conventional RRT in exploration efficiency.

Robustness demonstrated through both simulation and real-world tests.

Abstract

With the increasing need for multi-robot for exploring the unknown region in a challenging environment, efficient collaborative exploration strategies are needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree (RRT) exploration can be deployed to explore an unknown environment. However, its' greedy behavior causes multiple robots to explore the region with the highest revenue, which leads to massive overlapping in exploration process. To address this issue, we present a temporal memory-based RRT (TM-RRT) exploration strategy for multi-robot to perform robust exploration in an unknown environment. It computes adaptive duration for each frontier assigned and calculates the frontier's revenue based on the relative position of each robot. In addition, each robot is equipped with a memory consisting of frontier assigned and share among fleets to prevent repeating assignment of same frontier. Through both simulation and actual deployment, we have shown the robustness of TM-RRT exploration strategy by completing the exploration in a 25.0m x 54.0m (1350.0m2) area, while the conventional RRT exploration strategy falls short.