矿井救援机器人的B样条-APF-BRRT路径规划方法

姜媛媛; 李宏伟; 路子佩

doi:10.13433/j.cnki.1003-8728.20220168

矿井救援机器人的B样条-APF-BRRT路径规划方法

doi: 10.13433/j.cnki.1003-8728.20220168

姜媛媛^{1, 2,},
李宏伟¹,
路子佩¹

1.
安徽理工大学电气与信息工程学院，安徽淮南　232000
2.
安徽理工大学环境友好材料与职业健康研究院（芜湖），安徽芜湖　241003

基金项目: 安徽省重点研究与开发计划（202104g01020012）与安徽理工大学环境友好材料与职业健康研究院研发专项（ALW2020YF1）

详细信息

作者简介:
姜媛媛（1982−），教授，博士，研究方向为机器人导航与控制、智能诊断及故障预测，jyyll672@163.com

中图分类号: TP242.6
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- 文章访问数: 117
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-15
- 刊出日期: 2023-11-30

A Path Planning Method for Mine Rescue Robot Based on B-spline-APF-BRRT Algorithm

1.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, Anhui, China
2.
Institute of Environmental Friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, Anhui, China

摘要

摘要: 针对矿井非结构化、形状狭长的地形结构以及传统BRRT算法规划路径曲折、转折点较多等路径质量较差问题，提出一种基于B样条-APF-BRRT算法的矿井救援机器人路径规划方法。首先根据BRRT路径中产生的目标点和矿井环境中障碍物信息，引入APF目标引力的思想，构建人工势场；然后利用Douglas-Peuker算法进行分线段处理，重新提取关键节点；最后使用B样条函数进行光滑拟合获得路径，减少了APF-BRRT算法所得路径的转折点和长度。对B样条-APF-BRRT算法进行实验，结果表明改进算法所获路径转折点和长度都明显优于BRRT算法，在矿井狭窄巷道中，相对于BRRT算法，B样条-APF-BRRT方法产生的路径更加平滑，转折次数减少到9次，路径长度减少了7.73%。
- 救援机器人 /
- 路径规划 /
- BRRT算法 /
- APF算法 /
- B样条函数
Abstract: To improve the poor path quality such as unstructured mine, long and narrow ground structure and traditional BRRT algorithm, a path planning method for a mine rescue robot based on the B-spline APF-BRRT algorithm is proposed. Firstly, according to the target points generated with the BRRT algorithm and the information on obstacles in the mine environment, the APF target gravity is used to construct the artificial potential field. Then the Douglas-Peuker algorithm is used for line segment processing to extract key nodes. Finally, the B-spline function is used to obtain the smooth fitting path, which reduces the number of turning points and the length of path obtained with the APF-BRRT algorithm. The experimental results on the B-spline -APF-BRRT algorithm show that the path's number of turning points and its length obtained with the improved algorithm are obviously better than those obtained with the BRRT algorithm. Compared with the BRRT algorithm, the path generated with the B-spline -APF-BRRT algorithm is smoother in a narrow mine tunnel, the number of turning points is reduced to 9, and the path length is reduced by 7.73%.
- rescue robot /
- path planning /
- BRRT algorithm /
- APF algorithm /
- B-spline function

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图 1 BRRT算法规划示意图

Figure 1. Schematic diagram of BRRT algorithm planning

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图 2 基于APF的移动机器人受力示意图

Figure 2. Schematic diagram of forces exerted by mobile robot based on APF

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图 3 B样条拟合曲线示意图

Figure 3. Schematic of APF-based mobile robot forces

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图 4 B样条-APF-BRRT算法流程图

Figure 4. Flowchart of B spline-APF-BRRT algorithm

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图 5 简单环境下B样条-APF-BRRT运动路径

Figure 5. B-spline-APF-BRRT motion path in a simple environment

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图 6 复杂环境下B样条-APF-BRRT运动路径

Figure 6. B-spline-APF-BRRT motion paths in complex environments

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图 7 狭窄巷道环境下B样条-APF-BRRT运动路径

Figure 7. B-spline-APF-BRRT motion path in narrow alleyenvironment

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图 8 简单环境下B样条-APF-BRRT运动路径

Figure 8. B-spline-APF-BRRT motion path in simple environments

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图 9 简单环境下B样条-BRRT运动路径

Figure 9. B-spline-BRRT motion paths in simple environments

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图 10 4种算法对比实验结果

Figure 10. Comparing experimental results on four algorithms

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表 1 3种算法在不同环境下的性能测试对比结果

Table 1. Comparison results of three algorithms′ performance tests in different environments

地图	简单环境	复杂环境	狭窄巷道
原始路径长度/m	688.31	805.38	862.30
本文路径长度/m	660.30	756.42	795.62
原始路径节点总数/个	27	34	37
提取的关键节点/个	9	11	11
原始路径转折次数/次	25	32	34
本文路径转折次数/次	7	9	9

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表 2 有无APF算法性能测试结果对比

Table 2. Comparison of performance test results with and without APF algorithm

算法	B-APF-BRRT	B-BRRT
路径长度/m	674.38	730.54
节点总数/个	9	11
转折次数/次	7	9

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表 3 本文算法与传统算法性能测试对比结果

Table 3. Comparison of the performance test results ofthis paper′s algorithm and the traditional algorithm

算法	本文算法	A*	GA	RRT
路径长度/m	601.34	592.27	740.00	720.79
时间/s	3.20	37.58	29.92	5.44
转折次数/次	3	7	1	20

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参考文献(20)

[1]	葛世荣. 煤矿机器人现状及发展方向[J]. 中国煤炭, 2019, 45(7): 18-27. GE S R. Present situation and development direction of coal mine robots[J]. China Coal, 2019, 45(7): 18-27. (in Chinese)
[2]	LI M G, ZHU H, YOU S Z, et al. UWB-Based localization system aided with inertial sensor for underground coal mine applications[J]. IEEE Sensors Journal, 2020, 20(12): 6652-6669. doi: 10.1109/JSEN.2020.2976097
[3]	JU C Y, LUO Q H, YAN X Z. Path planning using an improved A-star algorithm[C]//2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan). Jinan, China: IEEE, 2020: 23-26.
[4]	LI W Z, LIU J J, YAO S L. An improved Dijkstra′s algorithm for shortest path planning on 2D grid maps[C]//2019 IEEE 9th International Conference on Electronics Information and Emergency Communication (ICEIEC). Beijing, China: IEEE, 2019: 438-441.
[5]	CHEN X Y, DAI Y H. Research on an improved ant colony algorithm fusion with genetic algorithm for route planning[C]//2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chongqing, China: IEEE, 2020: 1273-1278.
[6]	KONG S, ZHANG L Q. A path planning algorithm for sweeping robot based on improved neural network[C]//2019 3rd International Conference on Electronic Information Technology and Computer Engineering (EITCE). Xiamen, China: IEEE, 2019: 359-362.
[7]	CHEN L, SHAN Y X, TIAN W, et al. A fast and efficient double-Tree RRT^*-Like sampling-based planner applying on mobile robotic systems[J]. IEEE/ASME Transactions on Mechatronics, 2018, 23(6): 2568-2578. doi: 10.1109/TMECH.2018.2821767
[8]	李兆强, 张时雨. 基于快速RRT算法的三维路径规划算法研究[J]. 系统仿真学报, 2022, 34(3): 503-511. doi: 10.16182/j.issn1004731x.joss.20-0829 LI Z Q, ZHANG S Y. Research on 3D path planning Algorithm based on fast RRT algorithm[J]. Journal of System Simulation, 2022, 34(3): 503-511. (in Chinese) doi: 10.16182/j.issn1004731x.joss.20-0829
[9]	姜媛媛, 陶德俊, 时美乐, 等. DP-B样条移动机器人路径光滑算法[J]. 机械科学与技术, 2020, 39(4): 554-560. doi: 10.13433/j.cnki.1003-8728.20190157 JIANG Y Y, TAO D J, SHI M L, et al. Path smoothing algorithm of mobile robot based on DP-B spline[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(4): 554-560. (in Chinese) doi: 10.13433/j.cnki.1003-8728.20190157
[10]	ZHAO X L, CAO Z Q, GENG W J, et al. Path planning of manipulator based on RRT-Connect and Bezier curve[C]//2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). Suzhou, China: IEEE, 2019: 649-653.
[11]	MASHAYEKHI R, IDRIS M Y I, ANISI M H, et al. Informed RRT*-Connect: an asymptotically optimal single-query path planning method[J]. IEEE Access, 2020, 8: 19842-19852. doi: 10.1109/ACCESS.2020.2969316
[12]	SHU X, NI F L, ZHOU Z, LIU Y C, LIU H, ZOU T. Locally guided multiple Bi-RRT* for fast path planning in narrow passages[C]//2019 IEEE International Conference on Robotics and Biomimetics (ROBIO). Dali, China: IEEE, 2019: 2085-2091.
[13]	刘奥博, 袁杰. 目标偏置双向RRT算法的机器人路径规划[J]. 计算机工程与应用, 2022, 58(6): 234-240. LIU A B, YUAN J. Robot path planning based on goal biased bidirectional RRT algorithm[J]. Computer Engineering and Applications, 2022, 58(6): 234-240. (in Chinese)
[14]	皇甫淑云. 矿井救灾机器人障碍物识别与路径规划研究[D]. 徐州: 中国矿业大学, 2020. HUANGFU S Y. Research on obstacle identification and path planning of mine disaster relief robot[D]. Xuzhou: China University of Mining and Technology, 2020. (in Chinese)
[15]	许万, 杨晔, 余磊涛, 等. 一种基于改进RRT^的全局路径规划算法[J]. 控制与决策, 2022, 37(4): 829-838. XU W, YANG Y, YU L T, et al. A global path planning algorithm based on improved RRT^[J]. Control and Decision, 2022, 37(4): 829-838. (in Chinese)
[16]	LI K Y, LU Y G, ZHANG Y C. Dynamic obstacle avoidance path planning of UAV based on improved APF[C]//2020 5th International Conference on Communication, Image and Signal Processing (CCISP). Chengdu, China: IEEE, 2020: 159-163.
[17]	黄肖文, 任彦. 基于APF-RRT融合算法的无人机航迹规划研究[J]. 绿色科技, 2020(6): 220-221. doi: 10.16663/j.cnki.lskj.2020.06.074 HUANG X W, REN Y. Design and realization of data dynamic distribution algorithm for HDFS[J]. Journal of Green Science and Technology, 2020(6): 220-221. (in Chinese) doi: 10.16663/j.cnki.lskj.2020.06.074
[18]	ZHU J, ZHAO S L, ZHAO R. Path planning for autonomous underwater vehicle based on artificial potential field and modified RRT[C]//2021 International Conference on Computer, Control and Robotics (ICCCR). Shanghai: IEEE, 2021: 21-25.
[19]	WU Z C, SU W Z, LI J H. Multi-robot path planning based on improved artificial potential field and B-spline curve optimization[C]//2019 Chinese Control Conference (CCC). Guangzhou: IEEE, 2019: 4691-4696.
[20]	WANG X L, ZHANG D. Selecting optimal threshold value of Douglas-Peucker algorithm based on curve fit[C]//2010 First International Conference on Networking and Distributed Computing. Hangzhou: IEEE, 2010: 251-254.