DART: Dual-level Autonomous Robotic Topology for Efficient Exploration in Unknown Environments
🤖 AI Summary
To address inefficient exploration caused by incomplete convex region identification and frequent backtracking in unknown environments, this paper proposes a two-layer topological navigation framework: a lower-layer graph models geometric connectivity, while an upper-layer graph explicitly encodes the completeness of convex region exploration; local artificial potential field (LAPF) control is integrated for efficient motion planning. This work pioneers the deep synergy between topological abstraction and potential-field-based control, effectively decoupling semantic completeness from geometric structure. In simulation, our method significantly reduces total exploration time and path length compared to state-of-the-art approaches. Ablation studies confirm the necessity of each component. Real-world experiments demonstrate superior robustness under challenging conditions—including poor mapping quality and the presence of inaccessible regions—validating its practical applicability and reliability.
Application Category
📝 Abstract
Conventional algorithms in autonomous exploration face challenges due to their inability to accurately and efficiently identify the spatial distribution of convex regions in the real-time map. These methods often prioritize navigation toward the nearest or information-rich frontiers -- the boundaries between known and unknown areas -- resulting in incomplete convex region exploration and requiring excessive backtracking to revisit these missed areas. To address these limitations, this paper introduces an innovative dual-level topological analysis approach. First, we introduce a Low-level Topological Graph (LTG), generated through uniform sampling of the original map data, which captures essential geometric and connectivity details. Next, the LTG is transformed into a High-level Topological Graph (HTG), representing the spatial layout and exploration completeness of convex regions, prioritizing the exploration of convex regions that are not fully explored and minimizing unnecessary backtracking. Finally, an novel Local Artificial Potential Field (LAPF) method is employed for motion control, replacing conventional path planning and boosting overall efficiency. Experimental results highlight the effectiveness of our approach. Simulation tests reveal that our framework significantly reduces exploration time and travel distance, outperforming existing methods in both speed and efficiency. Ablation studies confirm the critical role of each framework component. Real-world tests demonstrate the robustness of our method in environments with poor mapping quality, surpassing other approaches in adaptability to mapping inaccuracies and inaccessible areas.
Problem
Research questions and friction points this paper is trying to address.
Improves autonomous exploration by identifying convex regions efficiently
Reduces backtracking and incomplete exploration in unknown environments
Enhances motion control and adaptability in poor mapping conditions
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual-level topological analysis for efficient exploration
Local Artificial Potential Field enhances motion control
Reduces exploration time and travel distance significantly
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