SCOPE: Skeleton Graph-Based Computation-Efficient Framework for Autonomous UAV Exploration

TL;DR

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%.

Abstract

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.

Figures (8)

• Figure 1: System overview of the proposed SCOPE framework. The architecture integrates two subsystems: (1) Skeleton Graph Maintenance & Implicit Region Generation (Blue Blocks): The system constructs a skeleton graph and activates nodes associated with frontiers. Using geometric ray probes for depth estimation, activated nodes are clustered into Implicit Unknown Regions---abstract volumetric representations where clustered nodes likely lead to the same contiguous unknown area. (2) Skeleton-Based Hierarchical Planning (Yellow Blocks): The UAV searches for a proximal target in its vicinity. If found (Exist), the Proximal Planner executes rapid local exploration; otherwise (Not Exist), the Region-Sequence Planner optimizes a global sequence over implicit sub-regions to guide the UAV to the next strategic area.
• Figure 2: Region analysis: probes are cast from the frontiers associated with activated nodes into unknown space, and nodes are clustered based on their intersection patterns and spatial proximity.
• Figure 3: A schematic diagram of the region-sequence planner. During region-guided planning, it assumes exploration proceeds only through currently activated nodes into unknown regions, while inter-regional costs are computed on the skeletal graph.
• Figure 4: Simulation environments.
• Figure 5: Visualization of exploration paths and velocity for four algorithms in the Complex Office environment, corresponding respectively to (a) FUEL, (b) FAEP, (c) RACER, (d) FALCON, (e) Proposed.