Scholarlink

<p>The Aircraft Towing Car is essential in airport ground operations, with automation playing a crucial role in enhancing efficiency and safety. This study introduces an Enhanced Rapidly exploring Random Tree (RRT) algorithm to improve the operational capabilities of Autonomous Aircraft Towing Cars. A high-fidelity virtual testing environment was developed using the ROS/Gazebo platform to address practical operational conditions and spatial constraints. An integrated system automates tasks before and after towing engagement, focusing on dynamic path planning after coupling. The Enhanced RRT algorithm incorporates real-time path planning, target biasing, and dynamic map adjustments to navigate restricted zones while ensuring compliance with aviation safety standards. Validation was performed using a 2D maze map and a 2D cost map with restricted zones, comparing the proposed algorithm to traditional methods. Results demonstrate that the enhanced RRT achieves faster convergence, fewer iterations, and lower path costs, excelling in complex environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Towards Autonomous Aircraft Towing Car: Path Planning Based on Enhanced RRT Algorithm

  • 期刊论文

  • 发表日期：

    2025年7月24日

查看全文
  • Jae-eun Kim,
  • Sungwook Cho,
  • Yeondeuk Jung

摘要

The Aircraft Towing Car is essential in airport ground operations, with automation playing a crucial role in enhancing efficiency and safety. This study introduces an Enhanced Rapidly exploring Random Tree (RRT) algorithm to improve the operational capabilities of Autonomous Aircraft Towing Cars. A high-fidelity virtual testing environment was developed using the ROS/Gazebo platform to address practical operational conditions and spatial constraints. An integrated system automates tasks before and after towing engagement, focusing on dynamic path planning after coupling. The Enhanced RRT algorithm incorporates real-time path planning, target biasing, and dynamic map adjustments to navigate restricted zones while ensuring compliance with aviation safety standards. Validation was performed using a 2D maze map and a 2D cost map with restricted zones, comparing the proposed algorithm to traditional methods. Results demonstrate that the enhanced RRT achieves faster convergence, fewer iterations, and lower path costs, excelling in complex environments.