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Motion planning and searching strategy of a transverse ledge climbing robot based on force feedback

Published online by Cambridge University Press:  03 February 2025

Reno Pangestu
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Guan Xian Yu
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Chi-Ying Lin*
Affiliation:
Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
*
Corresponding author: Chi-Ying Lin; Email: [email protected]

Abstract

A transverse ledge climbing robot inspired by athletic locomotion is a customized robot aiming to travel through horizontal ledges in vertical walls. Due to the safety issue and complex configurations in graspable ledges such as horizontal, inclined ledges, and gaps between ledges, existing well-known vision-based navigation methods suffering from occlusion problems may not be applicable to this special kind of application. This study develops a force feedback-based motion planning strategy for the robot to explore and make feasible grasping actions as it continuously travels through reachable ledges. A contact force detection algorithm developed using a momentum observer approach is implemented to estimate the contact force between the robot’s exploring hand and the ledge. Then, to minimize the detection errors due to dynamic model uncertainties and noises, a time-varying threshold is integrated. When the estimated contact force exceeds the threshold value, the robot control system feeds the estimated force into the admittance controller to revise the joint motion trajectories for a smooth transition. To handle the scenario of gaps between ledges, several ledge-searching algorithms are developed to allow the robot to grasp the next target ledge and safely overcome the gap transition. The effectiveness of the proposed motion planning and searching strategy has been justified by simulation, where the four-link transverse climbing robot successfully navigates through a set of obstacle scenarios modeled to approximate the actual environment. The performance of the developed grasping ledge searching methods for various obstacle characteristics has been evaluated.

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Lin, C.-Y. and Tian, Y.-J., “Design of transverse brachiation robot and motion control system for locomotion between ledges at different elevations,” Sensors 8(11), 343 (2021).Google Scholar
Lin, C.-Y. and Hu, T.-C., “Locomotion control of ledge climbing robot using CPG control and infrared sensors,” Int. J. iRobot. 4(4), 4049 (2022).Google Scholar
Lin, C.-Y. and Lee, J.-M., “Multi-locomotion design and implementation of transverse ledge brachiation robot inspired by sport climbing,” Biomimetics 8(2), 204 (2023).CrossRefGoogle ScholarPubMed
Wang, Y., Jiang, H., Huh, T. M., Sun, D., Ruotolo, W., Miller, M. W., Roderick, W. R. T., Stuart, H. and Cutkosky, M. R., “SpinyHand: Contact load sharing for a human-scale climbing robot, ASME,” J. Mech. Robot. 11(3), 031009 (2019).CrossRefGoogle Scholar
Ling, F. Y. Y., Liu, M. and Woo, Y. C., “Construction fatalities in Singapore,” Int. J. Project Manag. 27(7), 717726 (2009).CrossRefGoogle Scholar
Patané, L., “Bio-inspired robotic solutions for landslide monitoring,” Energies 12(7), 1256 (2020).CrossRefGoogle Scholar
Seo, T., Jeon, Y., Park, C. and Kim, J.-W., “Survey on glass and façade-cleaning robots: Climbing mechanisms, cleaning methods, and applications,” Int. J. Precis. Eng. Manuf.-Green Tech. 6(2), 367376 (2019).CrossRefGoogle Scholar
Zheng, Z., Ding, N., Chen, H., Hu, X., Zhu, Z., Fu, X., Zhang, W., Zhang, L., Hazken, S., Wang, Z. and Zhao, M.CCRobot-V: A Silkworm-Like Cooperative Cable-Climbing Robotic System for Cable Inspection and Maintenance,” In: 2022 International Conference on Robotics and Automation (ICRA) (2022) pp. 167170.Google Scholar
Wei, D. and Ge, W., “Research on one bio-inspired jumping locomotion robot for search and rescue,” Int. J. Adv. Robot. Syst. 11(10), (2014).CrossRefGoogle Scholar
Austin, M. P., Harper, M. Y., Brown, J. M., Collins, E. G. and Clark, J. E., “Navigation for legged mobility: Dynamic climbing,” IEEE Trans. Robot. 36(2), 537544 (2020).CrossRefGoogle Scholar
Fu, K. S., Gonzalez, R. C. and Lee, C. S. G., “Robotics: Control, Sensing, Vision, and Intelligence. vol. 1 (McGraw-Hill, New York, 1987).Google Scholar
Ming, Z., Ma, Y., Zhan, L., He, J. and Liu, Y., “Façade protrusion recognition and operation-effect inspection methods based on binocular vision for wall-climbing robots,” Appl. Sci. 13(9), 5721 (2023).Google Scholar
Novotny, P. M. and Ferrier, N. J., “Using Infrared Sensors and the Phong Illumination Model to Measure Distances,” In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C), 2, (1999) pp. 16441649.Google Scholar
Benet, G., Blanes, F., Simó, J. E. and Pérez, P., “Using infrared sensors for distance measurement in mobile robots,” Robot. Auton. Syst. 40(4), 255266 (2002).CrossRefGoogle Scholar
Starr, J. W. and Lattimer, B. Y., “A comparison of IR stereo vision and LIDAR for use in fire environments,” Sensors, 14 (2012).Google Scholar
Giles, J. W., Bankman, I. N., Sova, R. M., Green, W. J., King, T. R. M., Marcotte, F. J., Duncan, D. D. and Millard, J. A.. “General-purpose lidar system model with experiment validation in fog, oil, and smoke conditions,” Proc. SPIE Int. Soc. Optical Eng. Proc. SPIE, 4484, 178185 (2002).Google Scholar
Kim, J.-H., Lee, J.-C. and Choi, Y.-R., “PiROB: Vision-based pipe-climbing robot for spray-pipe inspection in nuclear plants,” Int. J. Adv. Robot. Syst. 15(6), (2018).CrossRefGoogle Scholar
Lu, H., Zhang, H., Yang, S. and Zheng, Z., “Camera Parameters Auto-adjusting Technique for Robust Robot Vision,” In: 2010 IEEE International Conference on Robotics and Automation (2010) pp. 15181523.Google Scholar
Da Veiga, R. S., De Oliveira, A. S., De Arruda, L. V. R. and Neves, F., “Localization and Navigation of a Climbing Robot Inside a LPG Spherical Tank Based on Dual-LIDAR Scanning of Weld Beads,” In: Koubaa, A. (ed.), Robot Operating System (ROS). Studies in Computational Intelligence, 625 (2016).Google Scholar
Saund, B. and Berenson, D., “Motion planning for manipulators in unknown environments with contact sensing uncertainty,” Int. Conf. Robot. Autom. (ICRA) 11, 461474 (2020).Google Scholar
Saund, B., Choudhury, S., Srinivasa, S. and Berenson, D., “The blindfolded robot: A Bayesian approach to planning with contact feedback,” Springer Proc. Adv. Robot. 20, 443459 (2022).CrossRefGoogle Scholar
Khedekar, N., Mascarich, F., Papachristos, C., Dang, T. and Alexis, K., “Contact-based Navigation Path Planning for Aerial Robots,” In: 2019 International Conference on Robotics and Automation (ICRA, (2019) pp. 41614167.Google Scholar
Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M. and Inoue, H., “Motion planning for humanoid robots,” Springer Tracts Adv. Robot. 15, 365374 (2005).CrossRefGoogle Scholar
Kanajar, P., Caldwell, D. G. and Kormushev, P., “Climbing Over Large Obstacles with a Humanoid Robot via Multi-contact Motion Planning,” In: 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (2017) pp. 12021209.Google Scholar
Luo, J., Y. Zhang, K. Hauser, H. A. Park, M. Paldhe, C. S. G. lee, M. Grey, M. Stilman, O. Jun Ho, J. Lee, I. Kim and P. Oh “Robust Ladder-climbing with a Humanoid Robot with Application to the DARPA Robotics Challenge,” In: 2014 IEEE International Conference on Robotics and Automation (ICRA), (2014) pp. 27922798.Google Scholar
Ding, L., Xing, H., Gao, H., Torabi, A., Li, W. and Tavakoli, M., “VDC-based admittance control of multi-DOF manipulators considering joint flexibility via hierarchical control framework,” Control Eng. Practice 124, 105186 (2022).CrossRefGoogle Scholar
Martínez-Rosas, J. C. and Arteaga, M. A., “Force and velocity observers for the control of cooperative robots,” Robotica 26(1), 8592 (2008).CrossRefGoogle Scholar
Li, Y., Li, Y., Zhu, M., Xu, Z. and Mu, D., “A nonlinear momentum observer for sensorless robot collision detection under model uncertainties,” Mechatronics 78, 102603 (2021).CrossRefGoogle Scholar
Li, T., Xing, H., Hashemi, E., Taghirad, H. D. and Tavakoli, M., “A brief survey of observers for disturbance estimation and compensation,” Robotica 41(12), 38183845 (2023).CrossRefGoogle Scholar
Cao, P., Gan, Y. and Dai, X., “Model-based sensorless robot collision detection under model uncertainties with a fast dynamics identification,” Int. J. Adv. Robot. Syst. 16(3), 115 (2019).CrossRefGoogle Scholar
Long, S., Dang, X., Sun, S., Wang, Y. and Gui, M., “A novel sliding mode momentum observer for collaborative robot collision detection,” Machines 10(9), 818 (2022).CrossRefGoogle Scholar
Fujiki, T. and Tahara, K., “Series admittance-impedance controller for more robust and stable extension of force control,” ROBOMECH J. 9(23), (2022).CrossRefGoogle Scholar
Otote, D. A., Li, B., Ai, B., Gao, S., Xu, J., Chen, X. and G., L., “A decision-making algorithm for maritime search and rescue plan,” Sustainability-Basel 11(7), 2084 (2019).CrossRefGoogle Scholar
Jiang, J., Yao, L., Huang, Z., Yu, G., Wang, L. and Bi, Z., “The state of the art of search strategies in robotic assembly,” J. Ind. Inf. Integr. 26, 100259 (2022).Google Scholar
Joo, Y., H. Kim, J. Park, C. Shin, H. Kwak, J. Song and C. Shin “Sensorless Force Control Algorithm Based on Momentum Observer Technique,” In: 2021 21st International Conference on Control, Automation and Systems (ICCAS), 17471751 (2021).Google Scholar
Li, W., Han, Y., Wu, J. and Xiong, Z., “Collision detection of robots based on a force/torque sensor at the bedplate,” IEEE/ASME Trans. Mechatron. 25(5), 25652573 (2020).CrossRefGoogle Scholar
Varga, B., Tar, J. K. and Horváth, R., “Fractional order inspired iterative adaptive control,” Robotica 42(2), 482509 (2024).CrossRefGoogle Scholar
Official Website of the International Federation of Sport Climbing. Available online: https://www.ifsc-climbing.org/. Accessed January 23, 2025.Google Scholar
Lin., C.-Y. and Yang, Z.-H., “TRBR: Flight body posture compensation for transverse ricochetal brachiation robot,” Mechatronics 65, 102307 (2020).CrossRefGoogle Scholar
Olsen, M. M. and Petersen, H. G., “A new method for estimating parameters of a dynamic robot model,” IEEE Trans. Robot. Autom. 17(1), 95100 (2001).CrossRefGoogle Scholar
Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling and Control (John Wiley & Sons, United States, 2020).Google Scholar
Tsai, L.-W.. Robot Analysis: The Mechanics of Serial and Parallel Manipulators (John Wiley & Sons, Canada, 1999).Google Scholar
Song, Z., Yi, J., Zhao, D. and Li, X., “A computed torque controller for uncertain robotic manipulator systems: Fuzzy approach,” Fuzzy Sets Syst. 154(2), 208226 (2005).CrossRefGoogle Scholar
Le, T. D., Kang, H.-J., Suh, Y.-S. and Ro, Y.-S., “An online self-gain tuning method using neural networks for nonlinear PD computed torque controller of a 2-dof parallel manipulator,” Neurocomputing 116, 5361 (2013).CrossRefGoogle Scholar
Davis, J. H., “Luenberger observers,” Found. Determin. Stochast. Control, 245254 (2002).CrossRefGoogle Scholar
Johan, P., Gravdahl, J. T. and Pettersen, K. Y. Vehicle-Manipulator Systems. Advances in Industrial Control (Springer-Verlag, United Kingdom, 2014).Google Scholar
Ren, T., Dong, Y., Wu, D. and Chen, K., “Collision detection and identification for robot manipulators based on extended state observer,” Control Eng. Pract. 17, 144153 (2018).CrossRefGoogle Scholar
Llama, M. A., Kelly, R. and Santibanez, V., “Stable computed-torque control of robot manipulators via fuzzy self-tuning,” IEEE Trans. Syst. Man Cybernet. Part B (Cybernetics) 30(1), 143150 (2000).CrossRefGoogle ScholarPubMed
Shang, W. and Cong, S., “Nonlinear computed torque control for a high-speed planar parallel manipulator,” Mechatronics 19(6), 987992 (2009).CrossRefGoogle Scholar
Al-Shuka, H. F. N., Leonhardt, S., Zhu, W.-H., Song, R., Ding, C. and Li, Y., “Active impedance control of bioinspired motion robotic manipulators: An overview,” Appl. Bionics Biomech. 2018, 119 (2018).CrossRefGoogle ScholarPubMed
Yoshikawa, T.. Foundations of Robotics: Analysis and Control (MIT Press, 1990).CrossRefGoogle Scholar
Liu, C. and Li, Z., “Force tracking smooth adaptive admittance control in unknown environment,” Robotica 41(7), 19912011 (2023).CrossRefGoogle Scholar
Majeed, A. and Hwang, S. O., “A multi-objective coverage path planning algorithm for UAVs to cover spatially distributed regions in urban environments,” Aerospace 22(11), 4031 (2021).Google Scholar
Niroui, F., Zhang, K., Kashino, Z. and Nejat, G., “Deep reinforcement learning robot for search and rescue applications: Exploration in unknown cluttered environments,” IEEE Robot. Autom. Lett. 4(2), 610617 (2019).CrossRefGoogle Scholar
Mei, Y., Lu, Y.-H., Hu, Y. C. and Lee, C. S. G., “Deployment of mobile robots with energy and timing constraints,” IEEE Trans. Robot. Autom. Robot. (ICCAR) 22(3), 507522 (2006).Google Scholar
Wongwirat, O. and Anuntachai, A., “Searching Energy-efficient Route for Mobile Robot with Ant Algorithm,” In: IEEE 2011 11th International Conference on Control, Automation and Systems, Gyeonggi-do, Korea (South) (2006) pp. 507522.Google Scholar
Nishii, J., “An analytical estimation of the energy cost for legged locomotion,” J. Theor. Biol. 238(3), 636645 (2006).CrossRefGoogle ScholarPubMed
Garofalo, G., Mansfeld, N., Jankowski, J. and Ott, C., “Sliding Mode Momentum Observers for Estimation of External Torques and Joint Acceleration,” In: 2019 International Conference on Robotics and Automation (ICRA) (2019) pp. 61176123.Google Scholar