Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression

2025-09-09

AI Summary

This paper addresses the challenges of communication constraints and low exploration efficiency in ground–aerial heterogeneous robot collaboration for autonomous exploration in unknown, confined, or large-scale environments. Methodologically, we propose a task-driven graph-structured collaborative exploration framework comprising: (1) marsupial-inspired coordinated path planning that synergistically exploits the complementary mobility and sensing capabilities of aerial and ground robots; (2) a semantic-aware voxel map compression algorithm that selectively encodes and transmits task-critical regions based on exploration importance, enabling resolution-adaptive reconstruction; and (3) a lightweight map representation with bandwidth-adaptive transmission. Experimental results demonstrate that our approach achieves 27% higher exploration efficiency and reduces communication data volume by 64%, while maintaining high-fidelity mapping accuracy and effective collaborative planning—significantly outperforming state-of-the-art baselines.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Efficient exploration of unknown environments is crucial for autonomous robots, especially in confined and large-scale scenarios with limited communication. To address this challenge, we propose a collaborative exploration framework for a marsupial ground-aerial robot team that leverages the complementary capabilities of both platforms. The framework employs a graph-based path planning algorithm to guide exploration and deploy the aerial robot in areas where its expected gain significantly exceeds that of the ground robot, such as large open spaces or regions inaccessible to the ground platform, thereby maximizing coverage and efficiency. To facilitate large-scale spatial information sharing, we introduce a bandwidth-efficient, task-driven map compression strategy. This method enables each robot to reconstruct resolution-specific volumetric maps while preserving exploration-critical details, even at high compression rates. By selectively compressing and sharing key data, communication overhead is minimized, ensuring effective map integration for collaborative path planning. Simulation and real-world experiments validate the proposed approach, demonstrating its effectiveness in improving exploration efficiency while significantly reducing data transmission.

Problem

• Marsupial ground-aerial robot team collaborative exploration
• Efficient exploration in confined large-scale limited communication environments
• Bandwidth-efficient task-driven map compression for spatial sharing

Innovation

• Marsupial ground-aerial robot team collaboration
• Task-driven map compression for bandwidth efficiency
• Graph-based path planning for optimal deployment