提出轻量骨架图框架，让无人机在低算力设备上高效探索未知环境。
SCOPE: Skeleton Graph-Based Computation-Efficient Framework for Autonomous UAV Exploration
- 构建实时骨架图，分层按需规划，减少全局优化频率。
- 相比顶尖方法，计算成本降低86.9%，轨迹更平滑稳定。
- 适合边缘部署，适用于资源受限的无人机自主探索场景。
在未知环境中实现自主探索是移动机器人感知、建图与决策的关键。然而，现有方法常依赖频繁的全局优化，导致计算延迟高、轨迹振荡，尤其在资源受限的边缘设备上表现不佳。为此，我们提出SCOPE框架，通过增量式构建实时骨架图，并引入隐式未知区域分析实现高效空间推理。规划层采用分层按需策略：近距规划器生成高频平滑局部轨迹，仅在必要时激活区域序列规划器优化全局访问顺序。仿真对比实验表明，SCOPE在探索性能上媲美最先进全局规划器，平均计算成本降低86.9%。真实世界实验进一步验证了系统在实际场景中的鲁棒性与低延迟特性。
原文摘要 · Abstract (English)
Autonomous exploration in unknown environments is key for mobile robots, helping them perceive, map, and make decisions in complex areas. However, current methods often rely on frequent global optimization, suffering from high computational latency and trajectory oscillation, especially on resource-constrained edge devices. To address these limitations, we propose SCOPE, a novel framework that incrementally constructs a real-time skeletal graph and introduces Implicit Unknown Region Analysis for efficient spatial reasoning. The planning layer adopts a hierarchical on-demand strategy: the Proximal Planner generates smooth, high-frequency local trajectories, while the Region-Sequence Planner is activated only when necessary to optimize global visitation order. Comparative evaluations in simulation demonstrate that SCOPE achieves competitive exploration performance comparable to state-of-the-art global planners, while reducing computational cost by an average of 86.9%. Real-world experiments further validate the system's robustness and low latency in practical scenarios.
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。