Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2025-01-05T14:58:20.808Z Has data issue: false hasContentIssue false
  • English
  • Français

Navigation of biped wall-climbing robots using BIM and ArUco markers

Published online by Cambridge University Press:  03 January 2025

Shichao Gu Open the ORCID record for Shichao Gu [Opens in a new window] ,
Ziyang Fu ,
Weinan Chen ,
Yisheng Guan ,
Hongmin Wu ,
Xuefeng Zhou  and
Haifei Zhu
Shichao Gu
Affiliation:
Guangzhou Maritime University, Guangzhou, China School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China Guangdong Key Laboratory of Modern Control Technology, Guangzhou, China
Ziyang Fu
Affiliation:
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Weinan Chen
Affiliation:
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Yisheng Guan
Affiliation:
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Hongmin Wu
Affiliation:
Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China Guangdong Key Laboratory of Modern Control Technology, Guangzhou, China
Xuefeng Zhou
Affiliation:
Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China Guangdong Key Laboratory of Modern Control Technology, Guangzhou, China
Haifei Zhu*
Affiliation:
School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
*
Corresponding author: Haifei Zhu; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Biped wall-climbing robots (BWCRs) serve as viable alternatives to human workers for inspection and maintenance tasks within three-dimensional (3D) curtain wall environments. However, autonomous climbing in such environments presents significant challenges, particularly related to localization and navigation. This paper presents a pioneering navigation framework tailored for BWCRs to navigate through 3D curtain wall environments. The framework comprises three essential stages: Building Information Model (BIM)-based map extraction, 3D climbing path planning (based on our previous work), and path tracking. An algorithm is developed to extract a detailed 3D map from the BIM, including structural elements such as walls, frames, and ArUco markers. This generated map is input into a proposed path planner to compute a viable climbing motion. For path tracking during actual climbing, an ArUco marker-based global localization method is introduced to estimate the pose of the robot, enabling adjustments to the target foothold by comparing desired and actual poses. The conducted experiments validate the feasibility and efficacy of the proposed navigation framework and associated algorithms, aiming to enhance the autonomous climbing capability of BWCRs.

Keywords

biped wall-climbing robotenvironment maplocalizationclimbing path correctionBIMArUco marker
Type
Research Article
Information
Robotica , First View , pp. 1 - 21
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Fang, Y., Wang, S., Bi, Q., Cui, D. and Yan, C., “Design and technical development of wall-climbing robots: A review,” J. Bionic Eng. 19(4), 1–25 (2022).CrossRefGoogle Scholar
Balaguer, C., Gimenez, A. and Abderrahim, C., “ROMA robots for inspection of steel-based infrastructures,” Ind. Robot Int. J. 29(3), 246–251 (2002).CrossRefGoogle Scholar
Balaguer, C., Gimenez, A., Huete, A. J., Sabatini, A. M., Topping, M. and Bolmsjo, G., “The mats robot: Service climbing robot for personal assistance,” IEEE Robot. Autom. Mag. 13(1), 51–58 (2006).CrossRefGoogle Scholar
Yang, C.-h. J., Paul, G., Ward, P. and Liu, D.. “A Path Planning Approach Via Task-objective Pose Selection with Application to an Inchworm-inspired Climbing Robot,” In: 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), IEEE (2016), pp. 401–406.Google Scholar
Guan, Y., Zhu, H., Wu, W., Zhou, X., Jiang, L., Cai, C., Zhang, L. and Zhang, H., “A modular biped wall- climbing robot with high mobility and manipulating function,” IEEE/ASME Trans. Mech. 18(6), 1787–1798 (2012).CrossRefGoogle Scholar
Zhu, H., Lu, J., Gu, S., Wei, S. and Guan, Y., “Planning three-dimensional collision-free optimized climbing path for biped wall-climbing robots,” IEEE/ASME Trans. Mech. 26(5), 2712–2723 (2020).CrossRefGoogle Scholar
Loc, V. G., Ig, M. K., Tran, D. T., Song, Y. K., Kim, H. M., Hyung, P. M. and Choi, H. R.. “Sensing and Control of Quadruped Walking and Climbing Robot Over Complex Environment,” In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (2008), pp. 3884–3889.Google Scholar
Howarth, B., Katupitiya, J., Guivant, J. and Szwec, A.. “ Novel Robotic 3D Surface Mapping Using Range and Vision Fusion,” In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (2010), 1539–1544.CrossRefGoogle Scholar
Paul, G., Quin, P., To, A. W. K. and Liu, D.. “A Sliding Window Approach to Exploration for 3D Map Building Using a Biologically Inspired Bridge Inspection Robot,” In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE (2015), pp.1097–1102.Google Scholar
Chen, W., Gu, S., Zhu, L., Zhang, H., Zhu, H. and Guan, Y., “Representation of truss-style structures for autonomous climbing of biped pole-climbing robots,” Robot. Auton. Syst. 101, 126–137 (2018).CrossRefGoogle Scholar
Cai, G., Lin, W., Wei, S., Gu, S., Zhu, H. and Guan, Y.. “Representation of 3D Structure for Path Planning with Biped Wall-climbing Robots,” In: 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), IEEE (2019), pp. 2250–2255.Google Scholar
Xiao, J. and Zhu, Z.. Cooperative Wall-climbing Robots in 3D Environments for Surveillance and Target Tracking (CITY COLL OF THE CITY UNIV OF NEW YORK, (2009).Tech. Rep.CrossRefGoogle Scholar
Tavakoli, M., Cabrita, G., Faria, R., Marques, L. and de Almeida, A. T., “Cooperative multi-agent mapping of three-dimensional structures for pipeline inspection applications,” Int. J. Robot. Res. 31(12), 1489–1503 (2012).CrossRefGoogle Scholar
Park, J., Cho, Y. K. and Martinez, D., “A BIM and UWB integrated mobile robot navigation system for indoor position tracking applications,” J. Constr. Eng. Project Manage. 6(2), 30–39 (2016).Google Scholar
Liu, J., Luo, J., Hou, J., Wen, D., Feng, G. and Zhang, X., “A bim based hybrid 3D indoor map model for indoor positioning and navigation,” ISPRS Int. J. Geo-Inf. 9(12), 747 (2020).CrossRefGoogle Scholar
Jiang, Q., Liu, Y., Yan, Y., Mao, X., Xu, H. and Jiang, X., “BIM-based 3-D multimodal reconstruction for substation equipment inspection images,” IEEE Trans. Instrum. Meas. 73, 1–14 (2024).Google Scholar
Chen, Z., Chen, K., Song, C., Zhang, X., Cheng, J. C. and Li, D., “Global path planning based on bim and physics engine for ugvs in indoor environments,” Automat. Constr. 139, 104263 (2022).CrossRefGoogle Scholar
Acar, O. and YaS¸ ar, C. F., “Autonomous climbing robot for tank inspection,” Procedia Comput. Sci. 158, 376–381 (2019).CrossRefGoogle Scholar
Shao, L., Yang, S., Liu, H. and Li, J.. “Research on Location Method of Pipe Climbing Robot based on Gyroscope,” In: 2018 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE (2018), pp. 238–242.Google Scholar
Yu, X., Zheng, W. and Ou, L., “CPR-SLAM: RGB-D SLAM in dynamic environment using sub-point cloud correlations,” Robotica 42(7), 2367–2387 (2024).CrossRefGoogle Scholar
Liao, T. I., Chen, S. S., Lien, C. C., Lin, H. C., Lu, M. F. and Chen, W. P., “Development of a high- endurance cleaning robot with scanning-based path planning and path correction,” Microsyst. Technol. 27(4), 1061–1074 (2021). doi: 10.1007/s00542-018-4048-2.CrossRefGoogle Scholar
Lin, Q., An, Z. and Yang, L.. “Rebooting Ultrasonic Positioning Systems for Ultrasound-incapable Smart devices,” In: 25th Annual International Conference on Mobile Computing and Networking, (2019) pp. 1–16.Google Scholar
Wu, Y., Chen, R., Li, W., Yu, Y., Zhou, H. and Yan, K., “Indoor positioning based on walking-surveyed wifi fingerprint and corner reference trajectory-geomagnetic database,” IEEE Sens. J. 21(17), 18964–18977 (2021).CrossRefGoogle Scholar
Jang, B., Kim, H. and wook Kim, J., “Survey of landmark-based indoor positioning technologies,” Inform. Fusion 89(2023), 166–188 (2022).CrossRefGoogle Scholar
Yin, J., Luo, D., Yan, F. and Zhuang, Y., “A novel lidar-assisted monocular visual slam framework for mobile robots in outdoor environments,” IEEE Trans. Instrum. Meas. 71, 1–11 (2022).Google Scholar
Quin, P., Paul, G., Alempijevic, A. and Liu, D.. “Exploring in 3D with a Climbing Robot: Selecting the Next Best Base Position on Arbitrarily-oriented Surfaces,” In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE (2016), pp. 5770–5775.Google Scholar
Gu, S., Zhu, H., Li, H., Guan, Y. and Zhang, H., “Optimal collision-free grip planning for biped climbing robots in complex truss environment,” Appl. Sci. 8(12), 2533 (2018).CrossRefGoogle Scholar
Shih, B. Y., Chen, C.-Y. and Chou, W. C., “Retracted: Obstacle avoidance using a path correction method for autonomous control of a biped intelligent robot,” J. Vib. Control 17(10), 1567–1573 (2011).CrossRefGoogle Scholar
Tavakoli, M., Marques, L. and de Almeida, A. T., “A low-cost approach for self-calibration of climbing robots,” Robotica 29(1), 23–34 (2011).CrossRefGoogle Scholar
Jiajun, L., Zhenguo, S. and Zhang, Q. C. W., “Error correction algorithm for manipulator of wall climbing robot with both ends having magnetic ad- sorption,” J. Tsinghua Univ. (Sci. Technol.) 54(2), 185–190 (2015).Google Scholar
Song, W., Wang, Z., Wang, T., Ji, D. and Zhu, S., “A path tracking method of a wall-climbing robot towards autonomous inspection of steel box girder,” Machines 10(4), 256 (2022).CrossRefGoogle Scholar
Romero-Ramirez, F. J., Munoz-Salinas, R. and Carnicer, R. M., “Speeded up detection of squared fiducial markers,” Image Vision Comput. 76, 38–47 (2018).CrossRefGoogle Scholar
Besl, P. J. and McKay, N. D., “Method for Registration of 3-d Shapes. In Sensor Fusion IV: Control Paradigms and Data Structures, vol. 1611. Spie, United States (1992) pp. 586–606.CrossRefGoogle Scholar
Pomerleau, F., Colas, F. and Siegwart, R., “A review of point cloud registration algorithms for mobile robotics,” Found. Trends® Robot 4(1), 1–104 (2015).CrossRefGoogle Scholar
Zhang, J., Yu, H., Deng, H., Chai, Z., Ma, M. and Zhong, X., “A robust and rapid camera calibration method by one captured image,” IEEE Trans. Instrum. Meas. 68(10), 4112–4121 (2018).CrossRefGoogle Scholar
Lu, X. X., “A Review of Solutions for Perspective-n-Point Problem in Camera Pose Estimation,” In: Journal of Physics: Conference Series. vol. 1087 (IOP Publishing, 2018) pp. 052009.Google Scholar
Zhu, H., Guan, Y., Wu, W., Zhang, L., Zhou, X. and Zhang, H., “Autonomous pose detection and alignment of suction modules of a biped wall-climbing robot,” IEEE/ASME Trans. Mech. 20(2), 653–662 (2014).CrossRefGoogle Scholar
Supplementary material: File

Gu et al. supplementary material 1

Gu et al. supplementary material
Download Gu et al. supplementary material 1(File)
File 13 MB
Supplementary material: File

Gu et al. supplementary material 2

Gu et al. supplementary material
Download Gu et al. supplementary material 2(File)
File 35.4 MB