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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (6): 1181-1190, 1198    DOI: 10.3785/j.issn.1008-973X.2022.06.016
    
Optimal design and experimental study of thrust adsorption wall-climbing robot
Chao-jun XUE1(),Hai-bo WANG1,2,*(),Yu-peng CHEN1
1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2. Technology and Equipment of Rail Transit Operation and Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China
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Abstract  

A kind of thrust adsorption wall-climbing robot with coaxial dual-rotor was introduced. The thrust adsorption mechanism and frame were optimized respectively to enhance the load capacity, reduce energy consumption and increase the endurance time. The aerodynamic parameters of the thrust adsorption mechanism, such as blade number, blade installation angle and spacing ratio, were controlled by the control variable method. The aerodynamic model of the robot under different aerodynamic parameters was established and the flow field was simulated and solved. Based on the simulation results, the optimal design of the thrust adsorption mechanism was completed. Based on topology optimization, the structural parameter optimization design of the robot frame was completed by building response surface combined with multi objective genetic algorithm (MOGA) optimization and direct adaptive single-objective (AS-O) optimization. Compared with the initial structure, the mass of upper and lower plates were decreased 55.62% and 25.39% respectively. The thrust adsorption mechanism and climbing ability of robot were tested respectively, and the experimental results show that the aerodynamic simulation results of the thrust adsorption mechanism are reliable. The deviation of the rotation center axis of the upper and lower rotors is closely related to the performance of the thrust adsorption mechanism, and the robot has a good wall climbing ability.

Key words： wall-climbing robot      aerodynamic model      flow field simulation      structural parameter optimization     
Received: 19 April 2021      Published: 30 June 2022
CLC:  TH 122  
Fund:  国家自然科学基金资助项目（51905451）；轨道交通运维技术与装备四川省重点实验室开放基金资助项目（2019YW002）
Corresponding Authors: Hai-bo WANG     E-mail: 861356816@qq.com;haibowang@home.swjtu.edu.cn
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Chao-jun XUE
Hai-bo WANG
Yu-peng CHEN
Cite this article:

Chao-jun XUE,Hai-bo WANG,Yu-peng CHEN. Optimal design and experimental study of thrust adsorption wall-climbing robot. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1181-1190, 1198.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.06.016     OR     https://www.zjujournals.com/eng/Y2022/V56/I6/1181


推力吸附爬壁机器人的优化设计与试验

介绍共轴式双旋翼推力吸附爬壁机器人，通过优化推力吸附机构和机架，增强负载能力、降低能耗、增加续航时间. 采用控制变量法控制推力吸附机构的气动参数如叶片数、桨叶安装角、间距比等，建立不同气动参数下机器人气动模型并进行流场仿真。基于仿真结果，完成推力吸附机构的优化设计；基于拓扑优化用构建响应面叠加多目标遗传优化算法（MOGA）、直接单目标自适应优化算法（AS-O）优化，完成机器人机架结构参数优化设计。与初始结构相比，机架上、下层板质量分别降低了55.62%、25.39%. 试验推力吸附机构和机器人攀爬能力，结果表明，推力吸附机构气动仿真结果可靠，上、下层旋翼旋转中心轴偏差与推力吸附机构性能关系密切，机器人具备良好壁面攀爬能力.


关键词： 爬壁机器人,  气动模型,  流场仿真,  结构参数优化 
Fig.1 Thrust adsorption wall-climbing robot
k/片 γ u γ d γ s
2 36.32 22.02 19.74
3 36.74 21.04 19.19
4 39.37 20.37 19.19
5 40.41 20.77 19.64
6 43.52 20.70 20.13
Tab.1 Relationship between blade number and γ of thrust adsorption mechanism
θ u/(°) θ d/(°) γ u γ d γ s
6 5 111.12 23.55 27.64
7 6 42.11 22.13 20.80
8 7 39.37 20.37 19.19
9 8 36.09 19.59 18.16
10 9 34.08 19.01 17.43
11 10 32.32 18.74 16.92
12 11 31.27 18.61 16.64
13 12 30.37 18.65 16.48
14 13 29.87 18.84 16.47
15 14 29.50 19.40 16.69
16 15 29.70 19.28 16.66
Tab.2 Relationship between installation angle and γ of thrust adsorption mechanism
H/ D γ u γ d γ s
0.10 47.12 31.77 27.19
0.15 32.82 22.52 19.02
0.18 30.80 20.36 17.43
0.19 29.43 19.91 16.92
0.20 29.26 19.64 16.74
0.23 28.96 19.37 16.55
0.25 29.47 19.03 16.49
0.30 29.87 18.84 16.47
Tab.3 Relationship between spacing ratio and γ of thrust adsorption mechanism
Fig.2 Simulated power and thrust of thrust adsorption wall-climbing robot
Fig.3 Velocity vector diagram of rotors
Fig.4 Pressure cloud diagram of rotors
Fig.5 Forces and constraints of upper and lower plates
Hz
阶数 f u f d 阶数 f u f d
1 360.9 225.8 4 368.2 433.9
2 364.0 391.7 5 447.6 666.9
3 368.2 391.8 6 843.4 885.1
Tab.4 First six natural frequencies of upper and lower plates before topology
Fig.6 Topology optimization of upper and lower plates
Fig.7 Initial structural design of upper and lower plates
Hz
阶数 f u f d 阶数 f u f d
1 425.9 220.3 4 1793.0 468.6
2 1057.2 466.8 5 2456.4 502.3
3 1057.4 468.6 6 2876.1 600.6
Tab.5 First six natural frequencies of upper and lower plates in initial design
Fig.8 Parameterization of upper and lower plate models
P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8
mm
初始值 3.0 60.0 38.0 3.0 76.0 21.0 114.0 25.5
上限 4.0 80.0 50.0 4.0 80.0 28.0 120.0 30.0
下限 1.5 10.0 30.0 1.5 50.0 20.0 51.0 8.0
取值 2.0 45.0 30.0 3.0 65.0 20.0 80.0 15.0
Tab.6 Parameter variation range of upper and lower plates
Fig.9 Distribution diagram of optimal Pareto solution set for upper plate
Fig.10 Distribution diagram for optimal Pareto solution set for lower plate
方案 P 1/mm P 2/mm P 3/mm d/mm f/Hz m/g
1 1.80 30.25 30.00 0.60 257.49 62.97
2 1.91 23.18 32.01 0.50 274.86 69.87
3 1.81 42.08 32.50 0.50 261.50 70.49
4 1.88 27.88 33.01 0.49 271.75 71.53
5 2.03 25.49 30.56 0.43 290.58 71.67
初始值 3.00 60.00 38.00 0.09 425.90 141.88
Tab.7 Direct optimization results of upper plate
方案 P 1/mm P 2/mm P 3/mm d/mm f/Hz m/g
1 1.75 43.80 30.05 0.60 248.04 63.67
2 1.79 35.99 30.01 0.60 255.64 63.72
3 1.73 47.61 30.03 0.60 244.55 63.77
4 1.76 43.73 30.04 0.60 248.59 63.78
5 1.79 37.90 30.02 0.60 254.21 63.79
初始值 3.00 60.00 38.00 0.09 425.90 141.88
Tab.8 Optimization results of response surface for upper slab construction
方案 P 4/mm P 5/mm P 6/mm P 7/mm P 8/mm d/mm f/Hz m/g
1 2.80 68.56 20.00 78.56 15.09 0.60 220.27 235.30
2 2.97 69.04 20.43 71.83 11.98 0.57 235.89 241.96
3 2.93 63.58 22.14 61.93 24.35 0.57 222.89 260.73
4 2.81 75.57 20.86 80.09 18.34 0.51 232.28 258.07
5 3.02 75.14 21.06 65.17 13.74 0.52 258.75 259.66
初始值 3.00 76.00 21.00 114.00 25.50 0.33 220.05 315.39
Tab.9 direct optimization results of lower plate
方案 P 4/mm P 5/mm P 6/mm P 7/mm P 8/mm d/mm f/Hz m/g
1 2.93 60.86 20.12 84.66 13.85 0.60 203.19 237.30
2 2.92 59.41 20.21 82.54 15.47 0.60 203.60 237.53
3 2.93 60.86 20.28 84.96 13.81 0.60 203.03 238.11
4 2.96 60.86 20.04 83.23 13.45 0.59 206.02 238.23
5 2.93 60.86 20.34 84.66 13.81 0.60 203.22 238.15
初始值 3.00 76.00 21.00 114.00 25.50 0.33 220.05 315.39
Tab.10 Optimization results of response surface for lower plate construction
Fig.11 Comparison of direct optimization results for upper plate and response surface optimization results
Fig.12 Comparison of direct optimization results for lower plate and response surface optimization
Fig.13 Top view of upper and lower baseplate
Fig.14 Test test platform
Fig.15 Comparison of experiment and simulation results on power and thrust for thrust adsorption wall-climbing robot
G l / kg N t / kw ψ t/
（g · w ?1） 		N s / kW N I / kW ψ s/
（g · w ?1） 		R ψ
0.557 0.091 6.121 0.039 0.049 11.283 0.542
1.310 0.243 5.391 0.141 0.178 7.340 0.734
1.701 0.338 5.033 0.209 0.265 6.430 0.783
2.256 0.478 4.720 0.319 0.404 5.587 0.845
3.000 0.706 4.249 0.489 0.619 4.847 0.877
3.702 0.953 3.885 0.671 0.849 4.359 0.891
4.201 1.144 3.672 0.811 1.027 4.092 0.897
4.765 1.338 3.561 0.980 1.241 3.841 0.927
5.283 1.570 3.365 1.143 1.447 3.651 0.922
5.758 1.784 3.228 1.301 1.647 3.496 0.923
6.240 2.056 3.035 1.468 1.858 3.358 0.904
Tab.11 Power, experimental value and simulation value of thrust
Fig.16 Variation of rotor thrust with installation error
Fig.17 Thrust adsorption wall-climbing robot under different tilt angles
Fig.18 Vertical wall climbing test of prototype
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