[1]王 安,黄志平,赵纯权,等.抓斗卸船机防碰撞预警及最佳抓取点规划研究[J].机械与电子,2025,(02):73-80.
　WANG An,HUANG Zhiping,ZHAO Chunquan,et al.Research on Collision Prevention Warning and Optimal Gripping Point Planning for Grab Ship Unloaders[J].Machinery & Electronics,2025,(02):73-80.
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抓斗卸船机防碰撞预警及最佳抓取点规划研究()
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年02期
页码:
73-80
栏目:
机电一体化
出版日期:
2025-02-28

文章信息/Info

Title:
Research on Collision Prevention Warning and Optimal Gripping Point Planning for Grab Ship Unloaders
文章编号:
1001-2257 ( 2025 ) 02-0073-08
作者:
王 安 1 ; 黄志平 1 ; 赵纯权 2 ; 马金阳 2 ; 万书亭 2
1. 国能常州发电有限责任公司,江苏 常州 213000 ;
2. 华北电力大学河北省电力机械装备健康维护与失效预防重点实验室,河北 保定 071003
Author(s):
WANG An1 ;  HUANG Zhiping1 ;  ZHAO Chunquan2 ;  MA Jinyang2 ;  WAN Shuting2
( 1.Guoneng Changzhou Power Generation Co. , Ltd. , Changzhou 213000 , China ;?
2.Hebei Key Laboratory of Electric Machinery Health Maintenance and Failure Prevention , North China Electric Power University , Baoding 071003 , China )
关键词:
抓斗卸船机; 清舱机; 防碰撞; PointNet++ ; 最佳抓取点
Keywords:
grab ship unloader ;  clearing machine ;  anti-collision ;  PointNet++ ;  best grab point
分类号:
U653.92 ;
文献标志码:
A
摘要:
基于运煤轮船船舱抓斗卸船机在卸船作业时可能发生碰撞危险和卸船作业后期效率提升问题,提出了一种在抓斗卸船机卸船作业时进行实时监控和防碰撞预警以及卸船作业后期最佳抓取点获取的方法。该方法通过搭载激光扫描仪的实验平台扫描作业获取原始三维点云数据,利用欧氏聚类算法将船舱内的清舱机、出舱口和工作人员作为碰撞障碍目标物点云,煤堆作为体积计算目标物进行分割。对分割后的碰撞障碍目标物点云使用经过 PointNet++ 神经网络模型训练后的数据集进行验证识别,并通过对碰撞障碍目标物进行位置检测,并与抓斗进行基于空间 OBB 距离碰撞计算和防碰撞区域安全等级划分,综合进行碰撞预警。结合卸船作业后期分割后的煤堆体积计算结果指导抓斗安全降落到最佳抓取点便于进行合理卸船路径规划,能有效减少清舱机对煤堆整形的工作量,确保抓斗卸船和清舱机安全作业的同时能有效提高整体卸船作业效率。
Abstract:
Aiming at the potential collision risk and efficiency improvement issues that may occur during the unloading operation of coal transport ship hold grab unloaders , a method for real-time monitoring and collision prevention warning , as well as obtaining the optimal gripping point in the later stage of unloading operations is proposed.This method obtains raw 3D point cloud data by scanning operations on an experimental platform equipped with a laser scanner.The Euclidean clustering algorithm is used to segment the point cloud of the cleaning machine , hatch , and staff in the cabin as collision obstacles , and the coal pile as volume calculation targets.The segmented collision obstacle target point cloud is validated and recognized using a dataset trained with a PointNet++neural network model.By detecting the position of the collision obstacle target , collision calculation based on spatial OBB distance and safety level division of the collision prevention area are performed with the grab bucket , and collision warning is comprehensively carried out.Based on the calculation results of the coal pile volume after segmentation in the later stage of unloading operations , the grab bucket is guided to safely land at the optimal grabbing point for reasonable unloading path planning.This can effectively reduce the workload of the cleaning machine for shaping the coal pile , ensure the safe operation of the grab bucket unloading and cleaning machine , and effectively improve the overall efficiency of unloading operations.

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备注/Memo

备注/Memo:
收稿日期: 2024-05-04
基金项目:国家自然科学基金资助项目( 52275109 );河北省自然科学基金资助项目( E2022502007 )
作者简介:王 安 ( 1981- ),男,江苏姜堰人,学士,高级工程师,研究方向为燃煤电厂输煤系统智能化;黄志平 ( 1978- ),男,江西南丰人,工程师,研究方向为燃煤电厂输煤系统智能化协同、卸船机斗轮机智能化控制;赵纯权 ( 1994- ),男,重庆荣昌人,硕士研究生,研究方向为燃煤电厂输煤系统智能化;马金阳 ( 2000- ),男,黑龙江哈尔滨人,硕士研究生,研究方向为燃煤电厂输煤系统智能化;万书亭 ( 1970- ),男,山西长治人,博士,教授,博士研究生导师,研究方向为燃煤电厂输煤系统智能化,通信作者。
更新日期/Last Update: 2025-03-10