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

2026-02-26

AI Summary

This work proposes a skeleton graph–driven lightweight exploration framework to address the high computational latency and trajectory oscillations in autonomous drones operating in unknown environments, which are often caused by frequent global optimizations. The approach incrementally constructs a real-time skeleton graph and integrates implicit unknown-space analysis to enable a hierarchical, on-demand planning mechanism: a high-frequency local planner generates short-horizon trajectories, while a region-sequence planner optimizes the global visitation order only when necessary. Experimental results demonstrate that the proposed method reduces computational overhead by 86.9% on average compared to state-of-the-art global planners, while maintaining comparable exploration performance. Real-world evaluations further confirm its low latency and robustness in dynamic, unstructured environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Heuristic Search
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• autonomous UAV exploration
• computational latency
• trajectory oscillation
• resource-constrained devices
• global optimization

Innovation

• skeletal graph
• Implicit Unknown Region Analysis
• hierarchical planning
• computation-efficient exploration
• autonomous UAV