Where to Fly, What to Send: Communication-Aware Aerial Support for Ground Robots

2025-12-05

AI Summary

This work addresses the challenge of collaborative exploration by multi-robot systems in unknown environments under bandwidth-constrained communication. Specifically, it focuses on aerial-ground coordination where UAVs must dynamically select which map information to transmit to ground robots to minimize their navigation cost and enhance global exploration efficiency. Methodologically, we propose a communication-aware framework featuring a Value-of-Information (VoI)-based information filtering mechanism, integrated with Mixed-Integer Linear Programming (MILP) for joint communication and motion optimization, and augmented by a utility-score-driven active exploration strategy. Our key contribution lies in unifying information transmission decisions with UAV path planning within a single semantic-aware, on-demand communication model under limited bandwidth. Experimental results demonstrate that the proposed approach significantly reduces the path cost incurred by ground robots in reaching targets—by an average of 32%—while simultaneously improving map completeness and exploration coverage.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this work we consider a multi-robot team operating in an unknown environment where one aerial agent is tasked to map the environment and transmit (a portion of) the mapped environment to a group of ground agents that are trying to reach their goals. The entire operation takes place over a bandwidth-limited communication channel, which motivates the problem of determining what and how much information the assisting agent should transmit and when while simultaneously performing exploration/mapping. The proposed framework enables the assisting aerial agent to decide what information to transmit based on the Value-of-Information (VoI), how much to transmit using a Mixed-Integer Linear Programming (MILP), and how to acquire additional information through an utility score-based environment exploration strategy. We perform a communication-motion trade-off analysis between the total amount of map data communicated by the aerial agent and the navigation cost incurred by the ground agents.

Problem

• Optimizing communication for aerial-ground robot collaboration
• Deciding what and when to transmit map data
• Balancing communication load with ground robot navigation efficiency

Innovation

• Value-of-Information decision for aerial transmission
• Mixed-Integer Linear Programming for transmission optimization
• Utility score-based exploration for efficient mapping