多无人机探索中，用连通性建图+连续分配，大幅减少绕路。
C$^2$-Explorer: Contiguity-Driven Task Allocation with Connectivity-Aware Task Representation for Decentralized Multi-UAV Exploration
- 构建连通图分解未知区域为独立任务单元
- 引入邻域惩罚机制，使任务分配更连续，减少跨区绕行
- 仿真和实飞验证，探索时间缩短43.1%，路径减少33.3%
在通信受限条件下，多无人机高效探索受制于任务表示与分配的不足。以往任务表示或需高通信开销，或难以适应复杂环境，常导致低效遍历。短时分配策略忽视时空连续性，引发非连续任务分配与频繁跨区绕行。为此，我们提出C²-Explorer，一种去中心化框架：通过连通图将未探索区域分解为独立任务单元，并设计基于图的邻域惩罚机制，引导连续任务分配，提升任务序列的时空连续性。大量仿真实验表明，该方法显著优于现有最先进基准，平均探索时间减少43.1%，路径长度缩短33.3%。真实飞行测试进一步验证了系统可行性。代码将于https://github.com/Robotics-STAR-Lab/C2-Explorer发布。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。