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基于RRT森林算法的高层消防无人机室内协同路径规划

陈锦涛 李鸿一 任鸿儒 鲁仁全

陈锦涛, 李鸿一, 任鸿儒, 鲁仁全. 基于RRT森林算法的高层消防无人机室内协同路径规划. 自动化学报, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c210368
引用本文: 陈锦涛, 李鸿一, 任鸿儒, 鲁仁全. 基于RRT森林算法的高层消防无人机室内协同路径规划. 自动化学报, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c210368
Chen Jin-Tao, Li Hong-Yi, Ren Hong-Ru, Lu Ren-Quan. Cooperative indoor path planning of multi-UAVs for high-rise fire fighting based on RRT-forest algorithm. Acta Automatica Sinica, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c210368
Citation: Chen Jin-Tao, Li Hong-Yi, Ren Hong-Ru, Lu Ren-Quan. Cooperative indoor path planning of multi-UAVs for high-rise fire fighting based on RRT-forest algorithm. Acta Automatica Sinica, 2021, 45(x): 1−12 doi: 10.16383/j.aas.c210368

基于RRT森林算法的高层消防无人机室内协同路径规划

doi: 10.16383/j.aas.c210368
基金项目: 国家自然科学基金(62033003, 62121004, 62003093), 广东特支计划本土创新创业团队(2019BT02X353), 广东省重点领域研发计划(2021B0101410005), 广东省研究生教育创新计划项目(2020SFKC028)资助
详细信息
    作者简介:

    陈锦涛:广东工业大学自动化学院硕士研究生. 主要研究方向为高层消防多无人机协同路径规划. E-mail: jintao0104@126.com

    李鸿一:广东工业大学自动化学院教授, 主要研究方向为智能控制, 协同控制及其应用. 本文通信作者. E-mail: lihongyi2009@gmail.com

    任鸿儒:广东工业大学自动化学院讲师. 2013年与2019年分别获中国科学技术大学自动化系学士与博士学位. 主要研究方向为无人自主系统智能控制与协同控制. E-mail: renhongru2019@gdut.edu.cn

    鲁仁全:广东工业大学自动化学院教授. 主要研究方向为无人自主系统协同控制理论与应用. E-mail: rqlu@gdut.edu.cn

Cooperative Indoor Path Planning of Multi-UAVs for High-Rise Fire Fighting Based on RRT-Forest Algorithm

Funds: Supported By the National Natural Science Foundation of China (62033003, 62121004, 62003093), the Local Innovative and Research Teams Project of Guangdong Special Support Program (2019BT02X353), the Key Area Research and Development Program of Guangdong Province (2021B0101410005), and the Graduate Education Innovation Program of Guangdong Province (2020SFKC028)
More Information
    Author Bio:

    CHEN Jin-Tao Master student at the School of Automation, Guangdong University of Technology. His main research interest is collaborative path planning for multi-UAVs in high-rise fire fighting and rescue scenarios

    LI Hong-Yi Professor at the School of Automation, Guangdong University of Technology. His research interest covers intelligent control, cooperative control and its applications. Corresponding author of this paper

    REN Hong-Ru Lecturer at the School of Automation, Guangdong University of Technology. He received his B.E. and Ph.D. degrees in control science and engineering from the University of Science and Technology of China in 2013 and 2019, respectively. His research interest covers intelligent control and cooperative control of unmanned systems

    LU Ren-Quan Professor at the School of Automation, Guangdong University of Technology. His research interest covers cooperative control theory and application of unmanned autonomous system

  • 摘要: 在多无人机协同执行高层消防救援任务的场景中, 室内复杂火场环境下路径规划是亟待解决难题之一. 本文针对快速搜索随机树算法 (Rapidly-exploring random tree, RRT) 搜索区域受限、耗时较长、结果可行性差等问题, 提出RRT森林算法. 通过随机选取根节点、生成随机树、连接合并随机树, 使高层消防多无人机在复杂室内环境下协同路径规划效率显著提高. 此外, 采用两次动态规划以及改进障碍物接近检测方法, 进一步提高路径的可行性. 最终, 通过仿真验证算法的有效性.
  • 图  1  基于RRT森林算法的多无人机路径规划方法流程图

    Fig.  1  Workflow of multi-UAVs path planning approach based on RRT-Forest

    图  2  基本RRT算法采样及搜索过程

    Fig.  2  Sampling and exploring process of basic RRT

    图  3  双向RRT连接过程

    Fig.  3  Connecting process of bidirection-RRT

    图  4  RRT森林算法两种工作模式

    Fig.  4  Two modes working modes of RRT-Forest algorithm

    图  5  多路径的导出方法

    Fig.  5  Export method for multiple paths

    图  6  改进的障碍物接近检测示意图

    Fig.  6  Schematic diagram of the improved obstacle proximity detection

    图  7  各算法在复杂环境下的运行结果

    Fig.  7  Performance of algorithms in complex environment

    图  8  各算法在复杂环境中的实验数据

    Fig.  8  Statistics of algorithms in complex environment

    图  9  RRT森林算法多路径规划

    Fig.  9  Multi-path planning by RRT-Forest

    图  10  单路径优化过程

    Fig.  10  Optimization of single path

    图  11  多路径优化过程

    Fig.  11  Optimization of multi-paths

    图  12  复杂环境下原碰撞检测与改进碰撞检测对比

    Fig.  12  Comparison between novel and original obstacle checking

    图  13  实际环境下改进碰撞检测效果

    Fig.  13  Result of novel obstacle check

    表  1  各算法搜索用时数据

    Table  1  Statistics of time used in exploring of each algorithm

    基本RRT 双向RRT RRT森林($N_{_\text{Tree}}=20$)
    上四分位数11.0064 3.5425 0.69081
    中位数7.9990 2.5683 0.48464
    下四分位数6.1111 1.8900 0.36128
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  • 收稿日期:  2021-04-27
  • 录用日期:  2021-11-17
  • 网络出版日期:  2021-12-19

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