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

TL;DR

SCOPE is a novel skeletal graph-based framework that enables resource-efficient, real-time autonomous UAV exploration with significantly reduced computational latency and robust performance in complex environments.

Contribution

It introduces a skeletal graph construction and hierarchical planning strategy that reduces computational costs while maintaining exploration effectiveness.

Findings

Achieves 86.9% reduction in computational cost compared to state-of-the-art methods.

Maintains competitive exploration performance in simulation environments.

Demonstrates robustness and low latency in real-world UAV experiments.

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.