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<p>Path planning for mobile robots in complex environments remains a critical challenge in autonomous robotics, where conventional meta-heuristic algorithms often constrain motion to sequential node-by-node progression. To overcome this limitation, this paper proposes a Direction-Assisted Enhanced Black-winged Kite Algorithm (DAEBKA) that enables non-sequential path transitions through synergistic integration of optimization and directional heuristics. DAEBKA enhances the optimization capacity of the Black-winged Kite Algorithm (BKA) through two strategies, and then utilizes directional heuristics to expand the robot's motion primitives and allow it to move to distant points in a single step. Lastly, a novel graph-based integration strategy is proposed to address the issue of how to introduce directional heuristics into the optimization process of the enhanced BKA algorithm (EBKA). Experimental simulations across different scenarios have been tested, and these results demonstrate DAEBKA's superior performance: Compared to MAACO<i>,</i> MsAACO, and IHMACO, DAEBKA reduces turns by 11.1–38.5% while maintaining consistent path lengths. These results confirm that DAEBKA is an alternative solution for practical robotic path planning, ensuring motion smoothness.</p>

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Direction-assisted enhanced black-winged kite algorithm for mobile robot path planning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月21日

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  • Honglin Wan,
  • Qilin Wu,
  • Zhaochun Peng,
  • Zheng Lu

摘要

Path planning for mobile robots in complex environments remains a critical challenge in autonomous robotics, where conventional meta-heuristic algorithms often constrain motion to sequential node-by-node progression. To overcome this limitation, this paper proposes a Direction-Assisted Enhanced Black-winged Kite Algorithm (DAEBKA) that enables non-sequential path transitions through synergistic integration of optimization and directional heuristics. DAEBKA enhances the optimization capacity of the Black-winged Kite Algorithm (BKA) through two strategies, and then utilizes directional heuristics to expand the robot's motion primitives and allow it to move to distant points in a single step. Lastly, a novel graph-based integration strategy is proposed to address the issue of how to introduce directional heuristics into the optimization process of the enhanced BKA algorithm (EBKA). Experimental simulations across different scenarios have been tested, and these results demonstrate DAEBKA's superior performance: Compared to MAACO, MsAACO, and IHMACO, DAEBKA reduces turns by 11.1–38.5% while maintaining consistent path lengths. These results confirm that DAEBKA is an alternative solution for practical robotic path planning, ensuring motion smoothness.