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

2025-05-29

AI Summary

To address inefficient information sharing and insufficient implicit coordination in multi-robot collaborative exploration of unknown environments under communication constraints, this paper proposes a two-tier communication-aware information sharing mechanism and an entropy-field-driven distributed exploration strategy. The method innovatively employs hierarchical communication-rate modeling, jointly evaluating frontier and robot-state uncertainty via entropy-field estimation. It supports low-bandwidth position broadcasting and high-bandwidth neighborhood map fusion, incorporating time-adaptive target assignment and implicit robot rendezvous. Dynamic graph networks coupled with entropy-field modeling enable real-time map fusion and decentralized cooperative decision-making. Simulation and real-world experiments demonstrate significant improvements over state-of-the-art approaches: exploration time is reduced and success rate increased, with measured exploration speed improved by 21.32% and success rate enhanced by 16.67%.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Agent Communication
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

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.

Problem

• Addresses communication constraints in multi-robot exploration systems
• Enhances information-sharing and exploration strategies for robots
• Improves exploration time and success rate in unknown environments

Innovation

• Two-layer communication-aware information-sharing strategy
• Entropy-field-based distributed exploration strategy
• Duration-adaptive goal-assigning module