Advance Search
ZHANG Liya,LI Chenxin,LIU Bin,et al. Obstacle avoidance algorithm based on sub-image segmentation and mapping point cloud space[J]. Coal Science and Technology,2024,52(S2):368−374. DOI: 10.12438/cst.2023-0948
Citation: ZHANG Liya,LI Chenxin,LIU Bin,et al. Obstacle avoidance algorithm based on sub-image segmentation and mapping point cloud space[J]. Coal Science and Technology,2024,52(S2):368−374. DOI: 10.12438/cst.2023-0948

Obstacle avoidance algorithm based on sub-image segmentation and mapping point cloud space

More Information
  • Received Date: January 19, 2024
  • The inspection of the working status of coal conveyor belts is an important part of ensuring safe production. As a conventional means of intelligent inspection, inspection robots are of great significance for ensuring the safe production of coal mines and realizing the reduction of personnel and the enhancement of safety underground. In order to improve the obstacle avoidance ability of mine inspection robots under long-distance and complex working conditions, adjust the travel route in real time and achieve the avoidance of obstacles, based on the inspection and obstacle avoidance system with the fusion of multi-source sensing of infrared cameras and lidars, the main work is as follows: A robot obstacle avoidance algorithm based on sub-image segmentation and mapping of point cloud space is proposed. Firstly, by taking infrared data as the boundary condition, the infrared image is divided into blocks to form sub-image units, and the point cloud space range is mapped with sub-images of different scales, thereby realizing the extraction of obstacle point clouds; and by using the projection method of each sub-image unit, the limitation of the three-dimensional point cloud in the target area is completed; then, by using the way of boundary constraints to reduce the total amount of point cloud data processing, the convergence speed of the algorithm and the extraction speed of the obstacle feature point clouds are improved. Finally, the simplification effect of the algorithm on the total amount of point clouds is verified through simulation analysis, the inversion accuracy of the maximum outer diameter of obstacles under different sub-image scales is simulated, and the real-time obstacle avoidance ability effect of the system applying this algorithm is verified. The experimental results show that when the side length of the sub-image is 10.0 mm, the maximum relative error is less than 1.53%, the convergence time of the algorithm is 1.243 s, and both the inversion accuracy of the obstacle outer diameter and the convergence speed meet the actual application requirements; the algorithm has a high accuracy rate and obstacle avoidance efficiency in static obstacle, dynamic obstacle and multi-robot obstacle avoidance environments, meets the needs of inspection robots for real-time environmental data collection and obstacle avoidance, and has a high practical application value.

  • [1]
    葛世荣,胡而已,裴文良. 煤矿机器人体系及关键技术[J]. 煤炭学报,2020,45(1):455−463.

    GE Shirong,HU Eryi,PEI Wenliang. Classification system and key technology of coal mine robot[J]. Journal of China Coal Society,2020,45(1):455−463.
    [2]
    王国法,富佳兴,孟令宇. 煤矿智能化创新团队建设与关键技术研发进展[J]. 工矿自动化,2022,48(12):1−15.

    WANG Guofa,FU Jiaxing,MENG Lingyu. Development of innovation team construction and key technology research in coal mine intelligence[J]. Journal of Mine Automation,2022,48(12):1−15.
    [3]
    宋锐,郑玉坤,刘义祥,等. 煤矿井下仿生机器人技术应用与前景分析[J]. 煤炭学报,2020,45(6):2155−2169.

    SONG Rui,ZHENG Yukun,LIU Yixiang,et al. Analysis on the application and prospect of coal mine bionic robotics[J]. Journal of China Coal Society,2020,45(6):2155−2169.
    [4]
    杨春雨,张鑫. 煤矿机器人环境感知与路径规划关键技术[J]. 煤炭学报,2022,47(7):2844−2872.

    YANG Chunyu,ZHANG Xin. Key technologies of coal mine robots for environment perception and path planning[J]. Journal of China Coal Society,2022,47(7):2844−2872.
    [5]
    王国法,杜毅博. 智慧煤矿与智能化开采技术的发展方向[J]. 煤炭科学技术,2019,47(1):1−10.

    WANG Guofa,DU Yibo. Development direction of intelligent coal mine and intelligent mining technology[J]. Coal Science and Technology,2019,47(1):1−10.
    [6]
    葛世荣,朱华. 危险环境下救援机器人技术发展现状与趋势[J]. 煤炭科学技术,2017,45(5):1−8,21.

    GE Shirong,ZHU Hua. Technical Status and development tendency of rescue robot in dangerous environment[J]. Coal Science and Technology,2017,45(5):1−8,21.
    [7]
    张旭辉,吕欣媛,王 甜,等. 数字孪生驱动的掘进机器人决策控制系统研究[J]. 煤炭科学技术,2022,50(7):36−49.

    ZHANG Xuhui,LYU Xinyuan,WANG Tian,et al. Research on decision control system of tunneling robot driven by digital twin[J]. Coal Science and Technology,2022,50(7):36−49.
    [8]
    张鹏. 智能矿山机器人协同管控[J]. 工矿自动化,2021,47(S2):43−44.

    ZHANG Peng. Collaborative control of robots in intelligent mine[J]. Industry and Mine Automation,2021,47(S2):43−44.
    [9]
    A-HEMANTH R,KALYAN B,MURTHY C. Mine rescue robot system:a review[J]. Procedia Earth and Planetary Science,2015,11(1):457−462.
    [10]
    KULKARNI M,DHARMADHIKARI M,TRANZATTO M,et al. Autonomous teamed exploration of subterranean environments using legged and aerial robots[J]. ar Xiv preprintar Xiv,2021,06(2):482−493.
    [11]
    MILLER I D,CLADERA F, COWLEY A,et al. Mine tunnel exploration using multiple quadrupedal robots[J]. IEEE Robotics and Automation Letters,2020,5(2):2840−2847. doi: 10.1109/LRA.2020.2972872
    [12]
    曹现刚,许 罡,吴旭东,等. 柔性轨道式环境巡检机器人设计原理与试验[J]. 煤炭科学技术,2022,50(6):303−312.

    CAO Xiangang,XU Gang,WU Xudong,et al. Design principles and experiments of flexible track-type environmental inspection robot[J]. Coal Science and Technology,2022,50(6):303−312.
    [13]
    李雨潭,李猛钢,朱华. 煤矿搜救机器人履带式行走机构性能评价体系[J]. 工程科学学报,2017,39(12):1913−1921.

    LI Yutan,LI Menggang,ZHU Hua. Performance evaluation system of the tracked walking mechanism of a coal mine rescue robot[J]. Chinese Journal of Engineering,2017,39(12):1913−1921.
    [14]
    由韶泽,朱华,赵勇,等. 煤矿救灾机器人研究现状及发展方向[J]. 工矿自动化,2017,43(4):14−18.

    YOU Shaoze,ZHU Hua,ZHAO Yong,et al. Research status of coal minie rescue robot and its development directio[J]. Industry and Mine Automation,2017,43(4):14−18.
    [15]
    朱华,由韶泽. 新型煤矿救援机器人研发与试验[J]. 煤炭学报,2020,45(6):2170−2181.

    ZHU Hua,YOU Shaoze. Research and experiment of a new type of coal mine rescue robot[J]. Journal of China Coal Society,2020,45(6):2170−2181.
    [16]
    孙霖. 携带机械臂的履带救援机器人设计与仿真实验研究[D]. 哈尔滨:哈尔滨工业大学,2020.

    SUN Lin. Design and simulation experiment research of tracked rescue robot with manipulator[D]. Harbin:Harbin Institute of Technology,2020.
    [17]
    CAI C,FERRARI S. Information-driven sensor path planning by approximate cell decomposition[J]. IEEE Transactions on Systems,Man and Cybernetics,2009,39(3):672−689. doi: 10.1109/TSMCB.2008.2008561
    [18]
    姜朋. 基于强化学习的室内移动机器人避障策略研究[D]. 杭州:浙江大学,2023.

    JIANG Peng. Research on indoor mobile robot obstacle avoidance strategy based on reinforcement learning[D]. Hangzhou:Zhejiang University,2023.
    [19]
    WANG N,GAO Y,CHEN H,et al. NAS-FCOS:Fast neural architecture search for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,2020:11943−11951.
    [20]
    HUANG S C ,LE T H ,JAW D W. DSNet:joint semantic learning for object detection in inclement weather conditions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021,43(8):2623−2633.
    [21]
    LI X,WANG W,WU L,et al. Generalized focal loss:learning qualified and distributed bounding boxes for dense object detection[J]. Advances in Neural Information Processing Systems,2020,33:21002−21012.
    [22]
    荣耀,曹琼,安晓宇,等. 综采工作面三维激光扫描建模关键技术研究[J]. 工矿自动化,2022,48(10):82−87.

    RONG Yao,CAO Qiong,AN Xiaoyu,et al. Research on key technologies of 3D laser scanning modeling in fully mechanized working face[J]. Journal of Mine Automation,2022,48(10):82−87.
    [23]
    LABBÉ M,MICHAUD F. RTAB ‐ Map as an open ‐ source lidar and visual simultaneous localization and mapping library for large‐scale and long‐term online operation[J]. Journal of Field Robotics,2019,36(2):416−446. doi: 10.1002/rob.21831
  • Related Articles

    [1]CHENG Cheng, WU Hongzhuang, LIU Songyong. Constraint optimization of shearer cutting path based on B-spline curve fitting and mayfly algorithm[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(S1): 269-279. DOI: 10.13199/j.cnki.cst.2022-1429
    [2]WANG Maosen, BAO Jiusheng, BAO Zhouyang, YIN Yan, WANG Xiangsai, GE Shirong. Research on mine underground inspection robot target detection algorithm based on pyramid structure and attention mechanism coupling[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(6): 206-215. DOI: 10.12438/cst.2023-1071
    [3]HE Lei, GUO Yongcun, ZHI Ya, WANG Shuang, LI Deyong, HU Kun, CHENG Gang. Accurate identification method of coal and gangue based on geometric feature constraints by DE-XRT[J]. COAL SCIENCE AND TECHNOLOGY, 2024, 52(5): 262-275. DOI: 10.12438/cst.2023-1276
    [4]ZHANG Yi, KANG Zhengming, FENG Hong, LI Fei, LI Xin, HAN Xue. Exploration characteristics of coal and rock boundary in horizontal well with Azimuth electromagnetic wave instrument PeriScope[J]. COAL SCIENCE AND TECHNOLOGY, 2023, 51(6): 158-167. DOI: 10.13199/j.cnki.cst.2021-1089
    [5]JIN Shukui, KOU Ziming, WU Juan. Research on path planning and tracking algorithm of inspection robot in coal mine water[J]. COAL SCIENCE AND TECHNOLOGY, 2022, 50(5).
    [6]LIU Peng, JING Jiangbo, WEI Huizi, YU Jing, LU Xiaolong, LIU Mingming. Construction of knowledge base and early warning inference of gas accident based on time-space constraints[J]. COAL SCIENCE AND TECHNOLOGY, 2020, 48(7).
    [7]ZHAO Yanling, FANG Shuodong, DA Hongzhi, XIAO Wu. Recognition of out-of-production boundary of crops in mining subsidence arable land based on improved OTSU algorithm[J]. COAL SCIENCE AND TECHNOLOGY, 2020, 48(4).
    [8]WANG Teng, LIU Chuanjie, ZHANG Yanlu. Optimal design of working device for roadway repairing machine under space constraints[J]. COAL SCIENCE AND TECHNOLOGY, 2020, 48(1).
    [9]XU Shaoyi, LI Mei, MAO Shanjun, ZHI Ning, LYU Pingyang. Study on escape route algorithm with constraints during coal mine fire[J]. COAL SCIENCE AND TECHNOLOGY, 2018, (5).
    [10]YUAN Xue-xu GUO Ying-hai SHEN Yu-in SHAO Yu-bao, . Study on Sequence Stratigraphic Division by Using Milankovitch Cycles as Constraints[J]. COAL SCIENCE AND TECHNOLOGY, 2013, (12).

Catalog

    Article views (20) PDF downloads (6) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return