MEF-Explore: Communication-Constrained Multi-Robot Entropy-Field-Based Exploration

TL;DR

This work tackles fast and reliable multi-robot exploration under communication constraints by introducing MEF-Explore, a distributed framework that combines a two-layer, communication-aware information-sharing strategy with an entropy-field-based exploration policy. The approach uses a dynamic graph to manage high-speed map-merging opportunities and continuous position sharing, while novel entropies blend frontier uncertainty with robot presence and attractive potential to guide exploration and trigger implicit rendezvous. A duration-adaptive goal-assignment module further stabilizes task allocation, enabling faster and more consistent exploration with higher success rates in both simulation and real-world deployments. The results demonstrate substantial improvements over state-of-the-art methods, highlighting MEF-Explore’s practical impact for real-world, communication-limited robotic teams.

Abstract

Collaborative multiple robots for unknown environment exploration have become mainstream due to their remarkable performance and efficiency. However, most existing methods assume perfect robots' communication during exploration, which is unattainable in real-world settings. Though there have been recent works aiming to tackle communication-constrained situations, substantial room for advancement remains for both information-sharing and exploration strategy aspects. In this paper, we propose a Communication-Constrained Multi-Robot Entropy-Field-Based Exploration (MEF-Explore). The first module of the proposed method is the two-layer inter-robot communication-aware information-sharing strategy. A dynamic graph is used to represent a multi-robot network and to determine communication based on whether it is low-speed or high-speed. Specifically, low-speed communication, which is always accessible between every robot, can only be used to share their current positions. If robots are within a certain range, high-speed communication will be available for inter-robot map merging. The second module is the entropy-field-based exploration strategy. Particularly, robots explore the unknown area distributedly according to the novel forms constructed to evaluate the entropies of frontiers and robots. These entropies can also trigger implicit robot rendezvous to enhance inter-robot map merging if feasible. In addition, we include the duration-adaptive goal-assigning module to manage robots' goal assignment. The simulation results demonstrate that our MEF-Explore surpasses the existing ones regarding exploration time and success rate in all scenarios. For real-world experiments, our method leads to a 21.32% faster exploration time and a 16.67% higher success rate compared to the baseline.

Figures (15)

• Figure 1: Overview of our proposed MEF-Explore
• Figure 2: Graphical representation of the proposed information-sharing strategy during four-robot exploration. $\mathscr{M}_i$ is each robot $i$'s local map, $\boxplus$ is the map merging operator, and the visualization of each robot is based on the current communication status between that robot and the others. Each circle represents the range of each robot's $r_\mathrm{comm}$. For instance, considering the top-left visualization for Robot 1's point of view, we can see the green-filled circle of Robot 2, which means a high-speed communication link $e_{1,2}$ is established between them. Meanwhile, Robot 3 and Robot 4 are red-filled, which means Robot 1 communicates with them via low-speed communication only.
• Figure 3: Graphical representation of three-robot exploration by the proposed entropy-field-based exploration strategy from Robot 1's point of view when robots have individual exploration and then rendezvous. Red squares are frontier centroids, and ${g}_\mathrm{cur}(1)$ is the current goal of Robot 1.
• Figure 4: Environments for simulation. The red-outlined, green-filled, blue-outlined, and yellow-outlined circles indicate initial positions for two-robot, three-robot, four-robot, and five-robot exploration, respectively.
• Figure 5: Simulation results on Map 1 of our proposed MEF-Explore, the MWF-CN, the MMPF, and the GVGExp

Theorems & Definitions (2)

• Theorem 1
• proof