Problem
Research questions and friction points this paper is trying to address.
partially connected networks
heterogeneous robot teams
information relay
mission-aware coverage
intermittent communication
DORA: Divergence-Oriented Data-Relay Algorithm for Partially Connected Robot Teams
🤖 AI Summary
This study addresses the challenge of balancing exploration and relay communication, as well as achieving semantic complementary target discovery for heterogeneous unmanned aerial vehicle (UAV) teams operating in partially connected networks. To this end, it pioneers the formalization of the Heterogeneous Multi-Agent Communication (HMAC) problem and proposes a Divergence-Oriented Relay Algorithm (DORA). By leveraging information-theoretic value to drive communication and quantifying task-relevant divergence to optimize multi-robot coverage, DORA overcomes the limitations of conventional time-based scheduling. This work integrates information theory, multi-agent coordination, and semantically heterogeneous perception. Validated through both simulations and physical experiments, the proposed approach reduces mission reporting time (MRT) analytical latency by 74.8% compared to baselines, significantly enhancing team collaboration efficiency.
📝 Abstract
Teams of unmanned aerial vehicles (UAVs) deployed for search and monitoring missions frequently operate as partially connected networks, forcing each robot to trade off exploring the environment against relaying information to teammates. This tradeoff is especially acute when robots are semantically heterogeneous: an observation that appears uninformative to the robot that made it may be critical to a teammate with complementary detection capabilities. In this work, we formalize this setting as the heterogeneous mission-aware coverage (HMAC) problem, which couples complete multi-robot coverage of an area with capability-constrained mission-relevant target (MRT) discovery under intermittent communication. We then present DORA, a divergence-oriented data-relay algorithm that drives communication by the value of information to the team rather than by discovery alone. DORA quantifies the mission-relevant divergence between a robot's current information state and its estimate of each teammate's knowledge, capturing mission relevance, discovery novelty, sensor uncertainty, and the age of information. We evaluate DORA in simulation across four environments with differing object densities and spatial structure, and validate it on a physical UAV platform. Our results show that DORA improves MRT resolution delay by up to 74.8% over traditional time-based communication scheduling methods.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Divergence-Oriented Data-Relay
Heterogeneous Mission-Aware Coverage
Partially Connected Networks
Value of Information
Age of Information
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