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The paper introduces SCOPE, a novel framework for autonomous UAV exploration that addresses the computational limitations of existing methods by using a real-time skeletal graph and Implicit Unknown Region Analysis. SCOPE employs a hierarchical on-demand planning strategy, with a Proximal Planner for local trajectories and a Region-Sequence Planner for global visitation order optimization, activated only when needed. Experimental results show that SCOPE achieves comparable exploration performance to state-of-the-art global planners while reducing computational cost by 86.9% in simulation and demonstrating robustness in real-world scenarios.
Achieve state-of-the-art autonomous UAV exploration performance with 87% less computation by using a hierarchical planner that only invokes global replanning when absolutely necessary.
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.
