Ye DAI(), Shikun LI, Xukun RUI, Chaofang XIANG, Xinlei NIE
Key Laboratory of Advanced Manufacturing Intelligent Technology of Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China
In recent years, the robot industry has developed rapidly, and researchers and enterprises have begun to pay more attention to this industry. People are barely familiar with climbing robots, a kind of special robot. However, from their practical value and scientific research value, climbing robots should studied further. This paper analyzes and summarizes the key technologies of climbing robots, introduces various kinds of climbing robots, and examines their advantages and disadvantages to provide a reference for future researchers. Many countries have studied climbing robots and made some achievements. However, due to the complexity of climbing robots, their climbing efficiency and accuracy need to be further improved. The new structure can improve the climbing efficiency. This paper analyzes climbing robots such as mechanical arms, magnetic attraction, and claws. Optimization methods and path planning can improve the accuracy of work. This paper involves some control methods, including complex intelligent control methods such as behavior-based robot control. This paper also investigates various kinematic planning methods and expounds and summarizes various path planning algorithms, including machine learning and reinforcement learning of artificial intelligence, ant colony algorithm, and other algorithms. Therefore, by analyzing the research status of climbing robots at home and abroad, this paper summarizes three important aspects of climbing robots, namely, structural design, control methods, and climbing strategies, elaborates on the achievements and existing problems of these key technologies, and looks forward to the future development trend and research direction of climbing robots.
The suction force generated by the vacuum pump generally has a sucker structure.
The adsorption force is large, which can realize miniaturization and is lightweight, but the contact surface needs to be smooth.
[18,19,22,25]
Electromagnetic adsorption
Magnetic attraction force can be generated by permanent magnets or electromagnets.
An electromagnet needs electric energy to maintain its adsorption, which is only suitable for magnetic conductivity contact surfaces.
[20,21,23,24]
Friction attachment
The attachment is realized by the friction force generated by the clamping contact structure of the manipulator.
The attachment can be applied to various types of contact surfaces, and the attaching effect is stable.
[27?29]
Adhesion material attachment
Adhesion force is generated between the adhesive material and the adhesive surface, and adhesion is achieved.
The loading capacity is strong and the adhesion is controllable, but the processing technology of the adhesive material is complicated.
[30?35]
Tab.1
Fig.2
Fig.3
Fig.4
Fig.5
Fig.6
Fig.7
Fig.8
Fig.9
Fig.10
Fig.11
Algorithm
Describe
Advantages
Disadvantages
Refs.
Ant colony algorithm
Imitating the behavior characteristics of an ant colony, the principle of this algorithm is a path search system based on a positive feedback mechanism.
The initial convergence speed of the algorithm is fast.
It easily falls into the local optimum, rather than the global optimum, and its stability is poor.
[94,95]
PSO algorithm
Imitating the behavior of birds looking for food, its basic principle is that individuals and groups cooperate and share information to obtain the optimal solution.
It can be used not only for single-robot planning but also for multi-robot path planning, and it has strong environmental adaptability.
The newly randomly generated particles may fall into the local optimum.
[96,97]
Genetic algorithm
Simulated organisms are developing in a more “adaptive” direction. It uses genetic operators to select, cross, and mutate to simulate evolution.
A genetic algorithm is a global path search algorithm, which can be improved with other intelligent algorithms.
Its calculation speed is slow, and the efficiency of global path search is relatively low.
[98]
Q-learning algorithm
It is an online learning algorithm, whose basic principle is that the robot rewards and punishes its actions by interacting with the environment and then learns to find the appropriate path.
In the learning, the system allows the agent to try and learn, explore the external environment, find a better way of doing things, and constantly update the Q value table with the feedback of the environment.
The convergence speed of the optimal solution is very slow, and the planning time, iteration times, and path length can be improved.
[99]
Artificial potential field method
The position has “attraction” to the robot. A “repulsive force” acts on the obstacle robot. Finally, the movement direction of the robot is changed by the resultant force acting on the robot itself.
The algorithm has a simple structure, can avoid obstacles in real-time, and is widely used in the path planning of local obstacle avoidance of a single robot.
Sometimes the moving body falls into the local optimal solution and stops or oscillates.
[100,101]
Tab.2
Fig.12
Fig.13
ACO
Ant colony
AMR
Autonomous mobile robot
APF
Artificial potential field
CNN
Convolution neural network
D?H
Denavit–Hartenberg
MDP
Markov decision process
RL
Reinforcement learning
1
S J Li. The study of mechanism for self-scramble robot. Thesis for the Master’s Degree. Chongqing: Chongqing University, 2008 (in Chinese)
2
L Jiang. Development and analysis of a bio-inspired modular biped climbing robot. Dissertation for the Doctoral Degree. Guangzhou: South China University of Technology, 2012 (in Chinese)
3
M Armada, P González de Santos, J Nieto, D Araujo. On the design and control of a self-propelling robot for hazardous environments. In: Proceedings of the 21st International Symposium on Industrial Robots. Copenhagen: IFS Publishing Ltd., 1990, 159–166
4
R Aracil , R Saltarén , Q Reinoso . Parallel robots for autonomous climbing along tubular structures. Robotics & Autonomous Systems, 2003, 42(2): 125–134
5
C Balaguer , A Gimenez , A Jardon . Climbing robots’ mobility for inspection and maintenance of 3D complex environments. Autonomous Robots, 2005, 18(2): 157–169
6
X L Lu , S P Zhao , X Y Liu , Y S Wang . Design and analysis of a climbing robot for pylon maintenance. Industrial Robot, 2018, 45(2): 206–219 https://doi.org/10.1108/IR-08-2017-0143
7
L Jiang, Y S Guan, C W Cai, H F Zhu, X F Zhou, X M Zhang. Gait analysis of a novel biomimetic climbing robot. Journal of Mechanical Engineering, 2010, 46(15): 17–22 (in Chinese) https://doi.org/10.3901/JME.2010.15.017
8
G Lee , H Kim , K Seo , J Kim , H S Kim . MultiTrack: a multi-linked track robot with suction adhesion for climbing and transition. Robotics and Autonomous Systems, 2015, 72(2): 207–216 https://doi.org/10.1016/j.robot.2015.05.011
X L Lu , S P Zhao , D P Yu , X Liu . Pylon-Climber: a novel climbing assistive robot for pylon maintenance. Industrial Robot, 2017, 44(1): 38–48 https://doi.org/10.1108/IR-06-2016-0172
11
B Chu , K Jung , C S Han , D Hong . A survey of climbing robots: locomotion and adhesion. International Journal of Precision Engineering and Manufacturing, 2010, 11(4): 633–647 https://doi.org/10.1007/s12541-010-0075-3
B Wang , X Xiong , J Duan , Z Wang , Z Dai . Compliant detachment of wall-climbing robot unaffected by adhesion state. Applied Sciences, 2021, 11(13): 5860 https://doi.org/10.3390/app11135860
14
W R Provancher , S I Jensen-Segal , M A Fehlberg . ROCR: an energy-efficient dynamic wall-climbing robot. IEEE/ASME Transactions on Mechatronics, 2011, 16(5): 897–906 https://doi.org/10.1109/TMECH.2010.2053379
15
T Seo , Y Jeon , C Park , J Kim . Survey on glass and facade-cleaning robots: climbing mechanisms, cleaning methods, and applications. International Journal of Precision Engineering and Manufacturing-Green Technology, 2019, 6(2): 367–376 https://doi.org/10.1007/s40684-019-00079-4
16
W T Chang , C J Lin , Y H Lee , H J Chen . Development of an observational checklist for falling risk assessment of high-voltage transmission tower construction workers. International Journal of Industrial Ergonomics, 2018, 68: 73–81
17
B Li , F Jin , S Gu , L Lu , L Z Xu . The preliminary study of wall climbing firefighting rescue robot for high-rise and super high-rise building. Applied Mechanics and Materials, 2013, 404(6): 569–574 https://doi.org/10.4028/www.scientific.net/AMM.404.569
18
H Dulimarta, R L Tummala. Design and control of miniature climbing robots with nonholonomic constraints. In: Proceedings of the 4th World Congress on Intelligent Control and Automation. Shanghai: IEEE, 2002, 3267–3271
19
K Sangbae , S Matthew , T Salomon , H Barrett , S Daniel , R C Mark . Smooth vertical surface climbing with directional adhesion. IEEE Transactions on Robotics, 2008, 24(1): 65–74 https://doi.org/10.1109/TRO.2007.909786
20
M Tavakoli , C Viegas , L Marques , J N Pires , A T de Almeida . OmniClimbers: omni-directional magnetic wheeled climbing robots for inspection of ferromagnetic structures. Robotics and Autonomous Systems, 2013, 61(9): 997–1007 https://doi.org/10.1016/j.robot.2013.05.005
21
A Peidró , M Tavakoli , J M Marín , Ó Reinoso . Design of compact switchable magnetic grippers for the HyReCRo structure-climbing robot. Mechatronics, 2019, 59(1): 199–212 https://doi.org/10.1016/j.mechatronics.2019.04.007
22
Z Q Ren. Design and study on climbing robot based on negative pressure suction. Thesis for the Master’s Degree. Baoding: North China Electric Power University, 2018 (in Chinese)
23
F M Gao , J C Fan , L B Zhang , J K Jiang , S J He . Magnetic crawler climbing detection robot basing on metal magnetic memory testing technology. Robotics and Autonomous Systems, 2020, 125: 103439 https://doi.org/10.1016/j.robot.2020.103439
24
R Wang, X W. Shen Climbing robot with high stability. CN Patent, CN112339877A. 2021-02-09 (in Chinese)
25
M Rachkov. Control of climbing robot for rough surfaces. In: Proceedings of the Third International Workshop on Robot Motion and Control. Bukowy Dworek: IEEE, 2002, 101–105
26
C Park , J Bae , S Ryu , J Lee , T Seo . R-track: separable modular climbing robot design for wall-to-wall transition. IEEE Robotics and Automation Letters, 2021, 6(2): 1036–1042 https://doi.org/10.1109/LRA.2020.3015170
27
B Y Tang. Research on the mechanical structure of self-scramble robot for wall cleaning. Dissertation for the Doctoral Degree. Beijing: Beijing University of Technology, 2005 (in Chinese)
28
H Kim , D Kim , H Yang , K Lee , K Seo , D Chang , J Kim . Development of a wall-climbing robot using a tracked wheel mechanism. Journal of Mechanical Science and Technology, 2008, 22(8): 1490–1498 https://doi.org/10.1007/s12206-008-0413-x
29
G P Lan. The research of multi-wheeled and legged robot for climbing the wall-corner. Thesis for the Master’s Degree. Chongqing: Southwest University, 2019 (in Chinese)
30
J W Huang , Y Liu , Y X Yang , Z Zhou , J Mao , T Wu , J Liu , Q Cai , C Peng , Y Xu , B Zeng , W Luo , G Chen , C Yuan , L Dai . Electrically programmable adhesive hydrogels for climbing robots. Science Robotics, 2021, 6(53): eabe1858 https://doi.org/10.1126/scirobotics.abe1858
31
Y H Liu , B Lim , J W Lee , J Park , T Kim , T Seo . Steerable dry-adhesive linkage-type wall-climbing robot. Mechanism and Machine Theory, 2020, 153: 103987 https://doi.org/10.1016/j.mechmachtheory.2020.103987
32
K Autumn , M Sitti , Y A Liang , A M Peattie , W R Hansen , S Sponberg , T W Kenny , R Fearing , J N Israelachvili , R J Full . Evidence for van der Waals adhesion in gecko setae. Proceedings of the National Academy of Sciences, 2002, 99(19): 12252–12256 https://doi.org/10.1073/pnas.192252799
33
O Unver , M Sitti . Tankbot: a palm-size, tank-like climbing robot using soft elastomer adhesive treads. The International Journal of Robotics Research, 2010, 29(14): 1761–1777 https://doi.org/10.1177/0278364910380759
34
Y S Li , A Ahmed , D Sameoto , C Menon . Abigaille II: toward the development of a spider-inspired climbing robot. Robotica, 2012, 30(1): 79–89 https://doi.org/10.1017/S0263574711000373
35
Y D Wu , X G Dong , J K Kim , C X Wang , M Sitti . Wireless soft millirobots for climbing three-dimensional surfaces in confined spaces. Science Advances, 2022, 8(21): eabn3431 https://doi.org/10.1126/sciadv.abn3431
36
W Lin, G Cai, S Wei, S Gu, H Zhu, Y Guan. Three-dimensional truss modelling for biped climbing robots. In: Proceedings of 2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019, 1204–1209 https://doi.org/10.1109/ROBIO49542.2019.8961404
37
W Jiang , R Liu , Y Yang , S Wan , X Guo , Z Jiao , H Yu . Design and implementation of a new climbing robot for high voltage transmission tower. IOP Conference Series Materials Science and Engineering, 2018, 428: 012071 https://doi.org/10.1088/1757-899X/428/1/012071
38
Y Z Yao , W Wang , Y Qiao , Z He , F Liu , X Li , X Liu , D Zou , T Zhang . A novel series-parallel hybrid robot for climbing transmission tower. Industrial Robot, 2021, 48(4): 577–588 https://doi.org/10.1108/IR-01-2021-0011
39
Y 39. Yoon, D Rus. Shady3D: a robot that climbs 3D trusses. In: Proceedings of 2007 IEEE International Conference on Robotics and Automation. Roma: IEEE, 2007, 4071–4076
40
M Tavakoli, A Marjovi, L Marques, A T de Almedia. 3DCLIMBER: a climbing robot for inspection of 3D human-made structures. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. Nice: IEEE, 2008, 4130–4135 https://doi.org/10.1109/IROS.2008.4651024
41
H X Guo, J H Zhang, T Wang, Y J Li, J Hong, Y Li. Design and control of an inchworm-inspired soft robot with Omega-arching locomotion. In: Proceedings of IEEE International Conference on Robotics and Automation. Singapore: IEEE, 2017, 4154–4159
42
R Z Xie, M J Su, Y H Zhang, M J Li, H F Zhu, Y S Guan. PISRob: a pneumatic soft robot for locomoting like an inchworm. In: Proceedings of IEEE International Conference on Robotics and Automation. Brisbane: IEEE, 2018, 3448–3453
43
J Wang , Y Xi , C Ji , J Zou . A biomimetic robot crawling bidirectionally with load inspired by rock-climbing fish. Journal of Zhejiang University-SCIENCE A, 2022, 23(1): 14–26 https://doi.org/10.1631/jzus.A2100280
44
Y Liu , L Wang , F Niu , F Niu , P Li , Y Li , T Mei . A track-type inverted climbing robot with bio-inspired spiny grippers. Journal of Bionic Engineering, 2020, 17(5): 920–931 https://doi.org/10.1007/s42235-020-0093-5
45
S Bian , Y Wei , F Xu , D Kong . A four-legged wall-climbing robot with spines and miniature setae array inspired by longicorn and gecko. Journal of Bionic Engineering, 2021, 18(2): 292–305 https://doi.org/10.1007/s42235-021-0032-0
46
B Liao , H B Zang , M Y Chen , Y Wang , X Lang , N Zhu , Z Yang , Y Yi . Soft rod-climbing robot inspired by winding locomotion of snake. Soft Robotics, 2020, 7(4): 500–511 https://doi.org/10.1089/soro.2019.0070
47
J Li , H J Zhuang , S Liu , H Zhou , Y H Hou , G Mo , Y P Zhong , C J Lu . Structure design of transmission tower climbing robot based on electromagnetic adsorption. Machine Tool & Hydraulics, 2020, 48(5): 44–48 https://doi.org/10.3969/j.issn.1001-3881.2020.05.010
48
L Liao. Climbing claw of a climbing robot. CN Patent, CN213414005U, 2021-06-11 (in Chinese)
49
Y B Li. Research on the power tower climbing robot for transmission line. Thesis for the Master’s Degree. Harbin: Harbin Institute of Technology, 2016 (in Chinese)
50
J. Li A transmission line tower climbing device. CN Patent, CN212700384U, 2021-03-16 (in Chinese)
51
R P Owen. A climbing robot for inspection of electricity pylons. Thesis for the Master’s Degree. Wales: University of Wales, 2003
52
W T Jiang. Research of climbing and overriding device for high voltage transmission tower climbing robot. Thesis for the Master’s Degree. Beijing: North China Electric Power University, 2019 (in Chinese)
53
J C Fauroux , J Morillon . Design of a climbing robot for cylindro-conic poles based on rolling self-locking. Industrial Robot, 2010, 37(3): 287–292 https://doi.org/10.1108/01439911011037695
54
Q L Du , X P Lu , Y K Wang , S Liu . The obstacle-surmounting analysis of a pole-climbing robot. International Journal of Advanced Robotic Systems, 2020, 17(6): 1–20 https://doi.org/10.1177/1729881420979146
55
Q L Du , Y Li , S N Liu . Design of a micro pole-climbing robot. International Journal of Advanced Robotic Systems, 2019, 16(3): 2–11 https://doi.org/10.1177/1729881419852813
56
T L Lam, Y Xu. A flexible tree climbing robot: Treebot—design and implementation. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation. Shanghai: IEEE, 2011, 5849–5854 https://doi.org/10.1109/ICRA.2011.5979833
57
H Khairam , Y M Choong , N S N Ismadi , W A F W Othman , A A A Wahab , S S N Alhady . Design and development of a low-cost pole climbing robot using Arduino Mega. Journal of Physics: Conference Series, 2021, 1969(1): 012008 https://doi.org/10.1088/1742-6596/1969/1/012008
58
H F Zhu , Y S Guan , W Q Wu , L Zhang , X Zhou , H Zhang . Autonomous pose detection and alignment of suction modules of a biped wall-climbing robot. IEEE/ASME Transactions on Mechatronics, 2014, 20(2): 653–662 https://doi.org/10.1109/TMECH.2014.2317190
59
D Cruz-Ortiz , M Ballesteros-Escamilla , I Chairez , A Luviano . Output second-order sliding-mode control for a gecko biomimetic climbing robot. Journal of Bionic Engineering, 2019, 16(4): 633–646 https://doi.org/10.1007/s42235-019-0051-2
60
W Zhang, E H Huang, L Z Gu, L Huang, Y Fang, S Chen. Design and intelligent control of flexible limbs of tower climbing robot. In: Proceedings of 2022 International Seminar on Computer Science and Engineering Technology. Indianapolis: IEEE, 2022 https://doi.org/10.1109/SCSET55041.2022.00011
61
M Hernando , M Alonso , C Prados , E Gambao . Behavior-based control architecture for legged-and-climber robots. Applied Sciences, 2021, 11(20): 9547 https://doi.org/10.3390/app11209547
62
Y L Geng. Research on control system of transmission tower climbing robot. Thesis for the Master’s Degree. Jinan: Shandong Jianzhu University, 2020 (in Chinese)
63
S Khan , S Prabhu . Design and fabrication of wheeled pole climbing robot with high payload capacity. IOP Conference Series: Materials Science and Engineering, 2018, 402: 012021 https://doi.org/10.1088/1757-899X/402/1/012021
64
S Narayanan, U Vinoop, M Satish, N G Yashwanth. Autonomous tree climbing robot (Treebot). In: Proceedings of IEEE International Conference on Computational Intelligence and Computing Research. Enathi: IEEE, 2013, 1–5 https://doi.org/10.1109/ICCIC.2013.6724241
65
P Yang, Y Y Zhang, H X Zheng. A new intelligent control strategy for the stair-climbing robot. Journal of Lanzhou University of Technology, 2017, 43(4): 93–97 (in Chinese)
66
Y Wang, Z J Zhang, Y C Zheng. Research and design of a wheeled climbing robot. Machinery Design & Manufacture, 2017, 33(10), 240–243, 247 (in Chinese)
67
W Wang. Development of a power tower climbing robot with obstacle crossing function. Thesis for the Master’s Degree. Chengdu: Southwest Jiaotong University, 2020 (in Chinese)
68
N J Baishya , B Bhattacharya , H Ogai , K Tatsumi . Analysis and design of a minimalist step climbing robot. Applied Sciences, 2021, 11(15): 7044 https://doi.org/10.3390/app11157044
69
S K Mahmood , S H Bakhy , M A Tawfik . Novel wall-climbing robot capable of transitioning and perching. IOP Conference Series: Materials Science and Engineering, 2020, 881(1): 012049 https://doi.org/10.1088/1757-899X/881/1/012049
70
S Q Peng. Design and research of vertical climbing robot. Thesis for the Master’s Degree. Tangshan: North China University of Science and Technology, 2020 (in Chinese)
71
Q Dong. Robot astronaut control method of climbing motion and compliance manipulation in space. Dissertation for the Doctoral Degree. Beijing: Beijing Institute of Technology, 2015 (in Chinese)
72
S Liu. Design and analysis of new-type climbing robot for transmission line tower. Thesis for the Master’s Degree. Chengdu: Southwest Jiaotong University, 2019 (in Chinese)
73
M P Austin , M Y Harper , J M Brown , E G Collins , J E Clark . Navigation for legged mobility: dynamic climbing. IEEE Transactions on Robotics, 2020, 36(2): 537–544 https://doi.org/10.1109/TRO.2019.2958207
74
O Hamdoun , L E Bakkali , F Z Baghli . Analysis and optimum kinematic design of a parallel robot. Procedia Engineering, 2017, 181: 214–220 https://doi.org/10.1016/j.proeng.2017.02.374
75
A Patwardhan , A Prakash , R G Chittawadigi . Kinematic analysis and development of simulation software for Nex dexter robotic manipulator. Procedia Computer Science, 2018, 133: 660–667 https://doi.org/10.1016/j.procs.2018.07.101
76
L Ding , H Gao , Z Deng , J Song , Y Liu , G Liu , K Lagnemma . Foot‒terrain interaction mechanics for legged robots: modeling and experimental validation. International Journal of Robotics Research, 2013, 32(13): 1585–1606 https://doi.org/10.1177/0278364913498122
77
A Sintov , T Avramovich , A Shapiro . Design and motion planning of an autonomous climbing robot with claws. Robotics and Autonomous Systems, 2011, 59(11): 1008–1019 https://doi.org/10.1016/j.robot.2011.06.003
78
S Chen, H Zhu, Y Guan, P Wu, J Hu, X Chen, H Zhang. Collision-free single-step motion planning of biped pole-climbing robots in spatial trusses. In: Proceedings of IEEE International Conference on Robotics and Biomimetics. Shenzhen: IEEE, 2013, 280–285 https://doi.org/10.1109/ROBIO.2013.6739472
79
Y Lu , K K Zhou , N J Ye . Design and kinemics/dynamics analysis of a novel climbing robot with tri-planar limbs for remanufacturing. Journal of Mechanical Science and Technology, 2017, 31(3): 1427–1436 https://doi.org/10.1007/s12206-017-0243-9
80
S Nam , J Oh , G Lee , J Kim , T W Seo . Dynamic analysis during internal transition of a compliant multi-body climbing robot with magnetic adhesion. Journal of Mechanical Science and Technology, 2014, 28(12): 5175–5187 https://doi.org/10.1007/s12206-014-1141-z
81
X Y, Liu X L, Lu S P Zhao. Kinematics and singularity analysis of power tower climbing robot. Journal of Machine Design, 2016, 33(5), 7–13 (in Chinese)
82
S M LaValle , J J Jr Kuffner . Randomized kinodynamic planning. The International Journal of Robotics Research, 2001, 20(5): 378–400 https://doi.org/10.1177/02783640122067453
83
R Kindel, D Hsu, J C Latombe, S Rock. Kinodynamic motion planning amidst moving obstacles. In: Proceedings of 2000 ICRA. Millennium Conference. San Francisco: IEEE, 2000, 537–543
84
B An , J Kim , F C Park . An adaptive stepsize RRT planning algorithm for open-chain robots. IEEE Robotics and Automation Letters, 2018, 3(1): 312–319 https://doi.org/10.1109/LRA.2017.2745542
85
O Adiyatov, H A Varol. A novel RRT*-based algorithm for motion planning in dynamic environments. In: Proceedings of IEEE International Conference on Mechatronics and Automation. Takamatsu: IEEE, 2017, 1416–1421
86
W Chen, S Gu, Y Guan, H Zhang, G Liu, H Tang. A multi-layered path planning algorithm for truss climbing with a biped robot. In: Proceedings of International Conference on Information and Automation. Ningbo: IEEE, 2016, 1200–1205 https://doi.org/10.1109/ICInfA.2016.7832002
87
T W Zhang , G H Xu , X S Zhan , T Han . A new hybrid algorithm for path planning of mobile robot. The Journal of Supercomputing, 2022, 78(3): 4158–4181 https://doi.org/10.1007/s11227-021-04031-9
88
Y Q Chen , J L Guo , H D Yang , Z Q Wang , H L Liu . Research on navigation of bidirectional A*algorithm based on ant colony algorithm. Journal of Supercomputing, 2021, 77(2): 1958–1975 https://doi.org/10.1007/s11227-020-03303-0
89
J Han , S Koenig . A multiple surrounding point set approach using Theta* algorithm on eight-neighbor grid graphs. Information Sciences, 2022, 582: 618–632 https://doi.org/10.1016/j.ins.2021.10.024
90
S K Huang , W J Wang , C H Sun . A path planning strategy for multi-robot moving with path-priority order based on a generalized Voronoi diagram. Applied Sciences, 2021, 11(20): 9650 https://doi.org/10.3390/app11209650
91
B W Gao , W Shen , H Guan , L T Zheng , W Zhang . Research on multistrategy improved evolutionary sparrow search algorithm and its application. IEEE Access, 2022, 10: 62520–62534 https://doi.org/10.1109/ACCESS.2022.3182241
M Dorigo, V Maniezzo, A Colorni. Positive Feedback as a Search Strategy. Technical Report 91-016. 1991
95
Y Y Liu , Z Hou , Y Y Tan , H Q Liu , C H Song . Research on multi-AGVs path planning and coordination mechanism. IEEE Access, 2020, 8: 213345–213356 https://doi.org/10.1109/ACCESS.2020.3039959
96
R Eberhart, J Kennedy. A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya: IEEE, 1995, 39–43
97
B Y Song , Z D Wang , L Zou . An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Applied Soft Computing, 2021, 100: 106960 https://doi.org/10.1016/j.asoc.2020.106960
98
M P Vicmudo, E P Dadios, R R P Vicerra. Path planning of underwater swarm robots using genetic algorithm. In: Proceedings of 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. Palawan: IEEE, 2014, 1–5
99
E S Low , P Ong , K C Cheah . Solving the optimal path planning of a mobile robot using improved Q-learning. Robotics and Autonomous Systems, 2019, 115: 143–161 https://doi.org/10.1016/j.robot.2019.02.013
100
O Khatib . Real-time obstacle avoidance system for manipulators and mobile robots. International Journal of Robotics Research, 1986, 5(1): 90–98 https://doi.org/10.1177/027836498600500106
101
H M Jayaweera , S Hanoun . A dynamic artificial potential field (D-APF) UAV path planning technique for following ground moving targets. IEEE Access, 2020, 8: 192760–192776 https://doi.org/10.1109/ACCESS.2020.3032929
102
H Bae , G Kim , J Kim , D W Qian , S Lee . Multi-robot path planning method using reinforcement learning. Applied Science, 2019, 9(15): 3057 https://doi.org/10.3390/app9153057
103
Q Chang. Study on the planning and stability of omnidirectional locomotion for quadruped robot. Dissertation for the Doctoral Degree. Beijing: Beijing Institute of Technology, 2016 (in Chinese)
104
Z Y, Zhao T. Zhang Research on visual navigation and positioning method of climbing robot for port machinery. Hoisting and Conveying Machinery, 2021, (7): 27–32 (in Chinese)
105
A Krishna Lakshmanan , R E Mohan , B Ramalingam , A V Le , P Veerajagadeshwar , K Tiwari , M Ilyas . Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot. Automation in Construction, 2020, 112: 103078 https://doi.org/10.1016/j.autcon.2020.103078
106
S Das , S K Mishra . A machine learning approach for collision avoidance and path planning of mobile robot under dense and cluttered environments. Computers & Electrical Engineering, 2022, 103: 108376 https://doi.org/10.1016/j.compeleceng.2022.108376
107
C H J Yang, G Paul, P Ward, D K Liu. A path planning approach via task-objective pose selection with application to an inchworm-inspired climbing robot. In: Proceedings of IEEE International Conference on Advanced Intelligent Mechatronics. Banff: IEEE, 2016, 401–406
108
M U Atique , R I Sarker , A R Ahad . Development of an 8DOF quadruped robot and implementation of inverse kinematics using Denavit‒Hartenberg convention. Heliyon, 2018, 4(12): e01053 https://doi.org/10.1016/j.heliyon.2018.e01053
109
Z Cai , Y Gao , W Wei , T Gao , Z Xie . Model design and gait planning of hexapod climbing robot. Journal of Physics: Conference Series. IOP Publishing, 2021, 1754(1): 012157 https://doi.org/10.1088/1742-6596/1754/1/012157
110
Y Guan, L Jiang, X Zhang, H Zhang. Climbing gaits of a modular biped climbing robot. In: Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Singapore: IEEE, 2009, 532–537
