Research on path planning for coal mine rescue robots
-
摘要:
针对煤矿救援机器人采用双向A*算法存在搜索效率低、路径安全性和平滑性差，及动态窗口法（DWA）融合全局路径规划算法存在实时寻路效率低等问题，提出了一种基于分层平滑优化双向A*引导DWA（HSTA*−G−DWA）算法的煤矿救援机器人路径规划方法。首先，将碰撞约束函数的调整机制引入双向A*算法中，以提高路径规划的安全性。其次，在双向A*算法的代价函数中增加归正因子函数，防止正反向搜索路径不相交的情况，同时为预估代价函数增加动态加权因子函数以剔除路径搜索过程中无关扩展节点的搜索，从而提升路径搜索效率。然后，利用分层平滑优化策略消除路径中的冗余点和转折角，以减少节点数量和路径长度，并提高路径平滑性。最后，若煤矿救援机器人按照初始全局路径行驶过程中探测到未知障碍物，则利用全局路径引导DWA实现局部动态避障。仿真实验结果表明：① 静态环境下HSTA*−G−DWA算法路径搜索时间较传统A*算法和双向A*算法分别平均减少了81.82%和64.63%，路径的安全性和平滑性更好。② 未知环境下HSTA*−G−DWA算法可实时避开环境中出现的未知障碍物，路径长度较快速扩展随机树（RRT）算法、改进A*算法和现有融合算法分别减少了10.34%，14.28%和2.45%，路径搜索时间较现有融合算法平均减少了70.48%。实验室环境下实验结果表明：① 静态环境下，HSTA*−G−DWA算法路径搜索时间较传统A*算法平均减少了58.75%，机器人边缘距障碍物的最小距离平均增加了0.71 m。② 未知环境下，相比于传统A*算法，HSTA*−G−DWA算法可实时避开环境中出现的未知障碍物且路径的平滑性更好。
Abstract:This paper proposes a path planning method for coal mine rescue robots based on a Hierarchical Smooth Optimization Bidirectional A* guided dynamic window approach (HSTA*-G-DWA) algorithm. The method addresses several limitations of the traditional bidirectional A* algorithm, including low search efficiency, poor path safety, and inadequate smoothness, as well as low real-time pathfinding efficiency when integrating the DWA with global path planning algorithms. Firstly, an adjustment mechanism of collision constraint function is incorporated into the bidirectional A* algorithm to improve path safety. Next, a correction factor is incorporated into the cost function of the Bidirectional A* algorithm to ensure that the forward and backward search paths intersect, preventing them from diverging. Additionally, a dynamic weighting factor is added to the estimated cost function to eliminate irrelevant expanded nodes during pathfinding, thus improving search efficiency. A hierarchical smoothing optimization strategy is employed to remove redundant points and sharp turns, reducing both the number of waypoints and the overall path length, while enhancing smoothness. Finally, if the robot detects unknown obstacles while traveling along the global path, the DWA, guided by the global path, enables dynamic local obstacle avoidance. Simulation results show that: ① In static environments, the path search time using the HSTA*-G-DWA algorithm is reduced by 81.82% and 64.63% on average compared to the traditional A* and bidirectional A* algorithms, respectively, with improved path safety and smoothness. ② In unknown environments, the HSTA*-G-DWA algorithm can avoid unknown obstacles in real time, reducing the path length by 10.34%, 14.28%, and 2.45% compared to the rapidly-exploring random tree (RRT) algorithm, the improved A* algorithm, and existing integrated algorithms, respectively. The average path search time is reduced by 70.48% compared to existing integrated algorithms. In laboratory environments, experimental results show: ① In static environments, the HSTA*-G-DWA algorithm reduces the path search time by 58.75% on average compared to the traditional A* algorithm, and the minimum distance between the robot's edge and obstacles increases by 0.71 m on average. ② In unknown environments, compared to the traditional A* algorithm, the HSTA*-G-DWA algorithm can avoid unknown obstacles in real time, resulting in smoother paths.
-
-
![]()
图 5 HSTA*−G−DWA算法流程
Figure 5. Hierarchical smoothing optimization A* guiding dynamic window approach(HSTA*-G-DWA) algorithm flow
![]()
图 7 煤矿未知场景下仿真结果对比
Figure 7. Comparison of simulation results in unknown coal mine scenes
![]()
图 8 煤矿环境下HSTA*−G−DWA与其他路径规划算法性能对比
Figure 8. Performance comparison of HSTA*-G-DWA and other path planning algorithms in coal mine environment
![]()
图 10 实验室静态环境实验结果
Figure 10. Experimental results in a static environment in the laboratory
![]()
图 11 实验室未知环境下实验结果
Figure 11. Experimental results in an unknown environment in the laboratory
表 1 静态场景仿真实验数据对比
Table 1 Data comparison of simulation experiment in static scenes
算法 路径转折
角数/个非光滑转折
角数/个与障碍物
最小距离/m搜索时间/s 传统A*算法 18 18 0 31.57 双向A*算法 16 16 0 16.23 HSTA*−G−DWA算法 4 0 0.83 5.74 
下载: 导出CSV
表 2 煤矿未知场景下仿真实验数据对比
Table 2 Data comparison of simulation experiments in unknown coal mine scenes
测试场景 非光滑转折角数/个 路径长度/m 与障碍物最小距离/m RRT算法 改进4领域
A*算法HSTA*−G−
DWA算法RRT算法 改进4领域
A*算法HSTA*−G−
DWA算法RRT算法 改进4领域
A*算法HSTA*−G−
DWA算法转弯巷道 38 51 0 66.78 77 60.09 0 0 1.11 连转巷道 46 5 0 77.99 80 69.72 0 0 1.17 转巷巷道 61 7 0 90.39 88 80.94 0 0 1.09
下载: 导出CSV
-
[1] ZHENG Li,YU Wenjie,LI Guangxu,et al. Particle swarm algorithm path-planning method for mobile robots based on artificial potential fields[J]. Sensors,2023,23(13). DOI: 10.3390/s23136082.
[2] 陈艺文,江文松,杨力,等. 基于运动约束的移动机器人路径规划[J]. 计算机集成制造系统,2023,29(4):1186-1193. CHEN Yiwen,JIANG Wensong,YANG Li,et al. Path planning based on motion constraints for mobile robot[J]. Computer Integrated Manufacturing Systems,2023,29(4):1186-1193.
[3] 朱洪波,殷宏亮. 分层平滑优化A*引导DWA用于机器人路径规划[J]. 电子测量与仪器学报,2024,38(9):155-168. ZHU Hongbo,YIN Hongliang. Hierarchical smoothing optimization A*-guided DWA for robot path planning[J]. Journal of Electronic Measurement and Instrumentation,2024,38(9):155-168.
[4] 彭育强,黄泽龙,李少伟. 基于动态窗口法的移动机器人自动避障导航研究[J]. 自动化仪表,2020,41(10):26-29,33. PENG Yuqiang,HUANG Zelong,LI Shaowei. Research on automatic obstacle avoidance navigation of mobile robot based on dynamic window approach[J]. Process Automation Instrumentation,2020,41(10):26-29,33.
[5] 郑维,王昊,王洪斌. 动态环境下基于自适应步长Informed−RRT*和人工势场法的机器人混合路径规划[J]. 计量学报,2023,44(1):26-34. DOI: 10.3969/j.issn.1000-1158.2023.01.05 ZHENG Wei,WANG Hao,WANG Hongbin. Adaptive step size Informed-RRT* and artificial potential field algorithm for hybrid path planning of robot[J]. Acta Metrologica Sinica,2023,44(1):26-34. DOI: 10.3969/j.issn.1000-1158.2023.01.05
[6] 陈尔奎,吴梅花,张英杰. 复杂环境下煤矿救灾机器人路径规划[J]. 煤炭技术,2018,37(10):301-304. CHEN Erkui,WU Meihua,ZHANG Yingjie. Path planning for coal mine rescue robot in complex environment[J]. Coal Technology,2018,37(10):301-304.
[7] XU Huimin,YU Gaohong,WANG Yimiao,et al. Path planning of mecanum wheel chassis based on improved A* algorithm[J]. Electronics,2023,12(8):1754. DOI: 10.3390/ELECTRONICS12081754
[8] LUO Qiang,WANG Haibao,ZHENG Yan,et al. Research on path planning of mobile robot based on improved ant colony algorithm[J]. Neural Computing and Applications,2020,32(6):1555-1566. DOI: 10.1007/s00521-019-04172-2
[9] 程晓明,刘银华,赵文政. 面向大型三维结构检测的多机器人覆盖路径规划方法[J]. 计算机集成制造系统,2023,29(1):246-253. CHENG Xiaoming,LIU Yinhua,ZHAO Wenzheng. Multi-robot coverage path planning for large 3D structure inspection[J]. Computer Integrated Manufacturing Systems,2023,29(1):246-253.
[10] 魏彤,龙琛. 基于改进遗传算法的移动机器人路径规划[J]. 北京航空航天大学学报,2020,46(4):703-711. WEI Tong,LONG Chen. Path planning for mobile robot based on improved genetic algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics,2020,46(4):703-711.
[11] 周伟,胡毅,刘进江,等. 基于Dijkstra算法的AMR集群协同调度研究[J]. 制造技术与机床,2023(6):175-179. ZHOU Wei,HU Yi,LIU Jinjiang,et al. Research on AMR cluster cooperative scheduling based on Dijkstra algorithm[J]. Manufacturing Technology & Machine Tool,2023(6):175-179.
[12] 张家旭,王志伟,郭崇,等. 面向动态障碍物场景的自主代客泊车路径规划[J]. 东南大学学报(自然科学版),2022,52(2):369-376. ZHANG Jiaxu,WANG Zhiwei,GUO Chong,et al. Autonomous valet parking path planning for dynamic obstacle scene[J]. Journal of Southeast University(Natural Science Edition),2022,52(2):369-376.
[13] 王梓强,胡晓光,李晓筱,等. 移动机器人全局路径规划算法综述[J]. 计算机科学,2021,48(10):19-29. DOI: 10.11896/jsjkx.200700114 WANG Ziqiang,HU Xiaoguang,LI Xiaoxiao,et al. Overview of global path planning algorithms for mobile robots[J]. Computer Science,2021,48(10):19-29. DOI: 10.11896/jsjkx.200700114
[14] 朱子祺,李创业,代伟. 基于G−RRT*算法的煤矸石分拣机器人路径规划[J]. 工矿自动化,2022,48(3):55-62. ZHU Ziqi,LI Chuangye,DAI Wei. Path planning of coal gangue sorting robot based on G-RRT* algorithm[J]. Journal of Mine Automation,2022,48(3):55-62.
[15] 黄健萌,吴宇雄,林谢昭. 移动机器人平滑JPS路径规划与轨迹优化方法[J]. 农业机械学报,2021,52(2):21-29,121. DOI: 10.6041/j.issn.1000-1298.2021.02.002 HUANG Jianmeng,WU Yuxiong,LIN Xiezhao. Smooth JPS path planning and trajectory optimization method of mobile robot[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(2):21-29,121. DOI: 10.6041/j.issn.1000-1298.2021.02.002
[16] 黄智榜,胡立坤,张宇,等. 基于改进跳点搜索策略的安全路径研究[J]. 计算机工程与应用,2021,57(1):56-61. DOI: 10.3778/j.issn.1002-8331.2003-0064 HUANG Zhibang,HU Likun,ZHANG Yu,et al. Research on security path based on improved hop search strategy[J]. Computer Engineering and Applications,2021,57(1):56-61. DOI: 10.3778/j.issn.1002-8331.2003-0064
[17] 陶德俊,姜媛媛,刘延彬,等. 煤矿救援机器人路径平滑算法研究[J]. 工矿自动化,2019,45(10):49-54. TAO Dejun,JIANG Yuanyuan,LIU Yanbin,et al. Research on path smoothing algorithm of coal mine rescue robot[J]. Industry and Mine Automation,2019,45(10):49-54.
[18] 张伟民,张月,张辉. 基于改进A*算法的煤矿救援机器人路径规划[J]. 煤田地质与勘探,2022,50(12):185-193. DOI: 10.12363/issn.1001-1986.22.04.0317 ZHANG Weimin,ZHANG Yue,ZHANG Hui. Path planning of coal mine rescue robot based on improved A* algorithm[J]. Coal Geology & Exploration,2022,50(12):185-193. DOI: 10.12363/issn.1001-1986.22.04.0317
[19] 徐保来,管贻生,苏泽荣,等. 改进动态窗口法的阿克曼移动机器人局部路径规划器[J]. 机电工程技术,2016,45(9):21-26. DOI: 10.3969/j.issn.1009-9492.2016.09.005 XU Baolai,GUAN Yisheng,SU Zerong,et al. A modified dynamic window approach to local path planning for the ackermann mobile robot[J]. Mechanical & Electrical Engineering Technology,2016,45(9):21-26. DOI: 10.3969/j.issn.1009-9492.2016.09.005
[20] 毛清华, 姚丽杰, 薛旭升. 煤矿履带式定向钻机路径规划算法[J]. 工矿自动化,2024,50(2):18-27. MAO Qinghua, YAO Lijie, XUE Xusheng. Path planning algorithm for tracked directional drilling rigs in coal mines[J]. Journal of Mine Automation,2024,50(2):18-27.
[21] 王倩,李俊丽,杨立炜,等. 改进蚁群融合DWA算法的移动机器人路径规划[J]. 兵工自动化,2023,42(4):79-84. WANG Qian,LI Junli,YANG Liwei,et al. Mobile robot path planning based on improved ant colony and DWA algorithm[J]. Ordnance Industry Automation,2023,42(4):79-84.
-
期刊类型引用(2)
1. 门汝佳，雷志鹏，吝伶艳，张国栋，赵瑞雪，朱剑飞，许春雨，宋建成，田慕琴. 矿用乙丙橡胶电缆绝缘电热老化状态评估. 工矿自动化. 2019(04): 67-71 . 
本站查看
2. 周岩，王鹏，辛罡，李波，王德志. MQHOA优化算法能级稳定过程及判据研究. 电子学报. 2019(06): 1337-1343 . 百度学术
其他类型引用(1)
计量
- 文章访问数: 50
- HTML全文浏览量: 19
- PDF下载量: 6
- 被引次数: 3
