C$^2$-Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration

2026-03-08

AI Summary

This work addresses the challenges of redundant trajectories and high coordination overhead in multi-UAV cooperative exploration under communication constraints, which often stem from inadequate task representation and allocation strategies lacking spatiotemporal continuity. To overcome these limitations, the authors propose a decentralized exploration framework that uniquely integrates connectivity-aware task representation with a continuity-driven assignment mechanism. By constructing a connectivity graph to decompose unknown regions into independent task units and incorporating a graph neural network-inspired neighborhood penalty to enhance spatiotemporal continuity in task sequencing, the approach significantly reduces unnecessary cross-region detours and improves system scalability. Simulations demonstrate a 43.1% reduction in average exploration time and a 33.3% decrease in total path length, while real-world flight experiments confirm the practical feasibility of the proposed method.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Efficient multi-UAV exploration under limited communication is severely bottlenecked by inadequate task representation and allocation. Previous task representations either impose heavy communication requirements for coordination or lack the flexibility to handle complex environments, often leading to inefficient traversal. Furthermore, short-horizon allocation strategies neglect spatiotemporal contiguity, causing non-contiguous assignments and frequent cross-region detours. To address this, we propose C$^2$-Explorer, a decentralized framework that constructs a connectivity graph to decompose disconnected unknown components into independent task units. We then introduce a contiguity-driven allocation formulation with a graph-based neighborhood penalty to discourage non-adjacent assignments, promoting more contiguous task sequences over time. Extensive simulation experiments show that C$^2$-Explorer consistently outperforms state-of-the-art (SOTA) baselines, reducing average exploration time by 43.1\% and path length by 33.3\%. Real-world flights further demonstrate the system's feasibility. The code will be released at https://github.com/Robotics-STAR-Lab/C2-Explorer

Problem

• multi-UAV exploration
• task allocation
• task representation
• connectivity
• spatiotemporal contiguity

Innovation

• contiguity-driven allocation
• connectivity-aware representation
• decentralized multi-UAV exploration
• graph-based task decomposition
• spatiotemporal contiguity