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<p>To address the trade-off between timeliness and optimality in complex-environment path planning, this work proposes DHE-RRT*, an enhanced Rapidly-Exploring Random Tree algorithm with discrete hierarchical exploration. The DHE-RRT* algorithm introduces a two-tier recursive exploration framework that discretizes continuous expansion directions into a finite set of headings, thereby facilitating efficient local space coverage. To further enhance sampling efficiency, a forced-neighborhood detection mechanism steers the tree growth toward critical regions shaped by obstacle geometries. Moreover, DHE-RRT* decouples the initial path generation from the subsequent bisection-based topological optimization, effectively reducing computational complexity while maintaining probabilistic completeness. Extensive simulations demonstrate that DHE-RRT* has excellent adaptability and robustness.</p>

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Optimal rapidly-exploring random tree via discrete hierarchical exploration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年9月24日

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  • Zeng Zhaoyang,
  • Hu Bowen,
  • Chang Yufang

摘要

To address the trade-off between timeliness and optimality in complex-environment path planning, this work proposes DHE-RRT*, an enhanced Rapidly-Exploring Random Tree algorithm with discrete hierarchical exploration. The DHE-RRT* algorithm introduces a two-tier recursive exploration framework that discretizes continuous expansion directions into a finite set of headings, thereby facilitating efficient local space coverage. To further enhance sampling efficiency, a forced-neighborhood detection mechanism steers the tree growth toward critical regions shaped by obstacle geometries. Moreover, DHE-RRT* decouples the initial path generation from the subsequent bisection-based topological optimization, effectively reducing computational complexity while maintaining probabilistic completeness. Extensive simulations demonstrate that DHE-RRT* has excellent adaptability and robustness.