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

TL;DR

C$^2$-Explorer is a decentralized framework that constructs a connectivity graph to decompose disconnected unknown components into independent task units and introduces a contiguity-driven allocation formulation with a graph-based neighborhood penalty to discourage non-adjacent assignments, promoting more contiguous task sequences over time.

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

Figures (4)

• Figure 1: System Architecture of C$^2$-Explorer. Within communication range, Drone 1 (the lowest-ID UAV) acts as a temporary host. It builds volumetric maps via LIO and extracts task units using a connectivity-aware representation. The host then solves a contiguity-regularized CVRP to generate per-UAV task sequences and disseminates them to the other drones. Each drone subsequently executes CP-guided planning for navigation. In parallel, drones exchange local map updates, states, and trajectories within communication range to reduce redundant exploration and support collision avoidance.
• Figure 2: Comparison of Connectivity-Aware and Traditional Task Representations. (a) and (c) contrast environmental modeling: (a) shows our connectivity graph modeling the topology of free and unknown spaces, while (c) uses uniform grid decomposition based on unknown voxel centroids. (b) and (d) compare task representation and allocation outcomes: (b) illustrates our connectivity-aware approach, treating spatially disjoint unknown regions as task units and excluding obstacle-enclosed areas. Conversely, (d)'s topology-agnostic approach merges disconnected regions and retains unreachable tasks.
• Figure 3: Benchmark Results of 4-UAV Decentralized Multi-UAV Exploration. Visualizations of the final executed flight trajectories for C$^2$-Explorer, RACER, and FAME are presented across three complex scenarios: (a) Cubicle Office, (b) Open-plan Office, and (c) Octa Maze. Additionally, the rightmost column presents curves illustrating the evolution of the environment coverage ratio against exploration time for each method.
• Figure 4: Ablation on Communication Range. Exploration time and total path length of C$^2$-Explorer under different communication ranges $r_{\mathrm{comm}}$.