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Attitude stability control system for a hexapod bionic robot under hybrid disturbances
Published online by Cambridge University Press: 16 February 2026
Abstract
Legged robots operating in irregular environments are often subjected to compound disturbances such as tilts and vibrations, which can degrade attitude stability and motion reliability. This paper presents a real-time disturbance-adaptive control framework for a hexapod robot. The proposed system integrates quaternion-based attitude estimation using an extended Kalman filter (EKF), a double-threshold pose classifier, and a modular gait library and is implemented on an embedded controller with a 2 ms control-loop latency. Analytical verification and laboratory experiments demonstrate that the proposed control loop achieves uniform ultimate boundedness (UUB) under deterministic hybrid disturbances composed of controlled tilt and vibration, with a mean recovery time of 5.7 s. These results demonstrate that a lightweight rule-based controller can ensure reliable posture recovery within the experimentally validated laboratory scenarios, providing a foundation for future extensions to more complex environments. The main contributions of this work are (1) a disturbance-adaptive gait selection architecture for quasi-static stabilization, (2) a noise-robust EKF-based attitude estimation and double-threshold pose determination scheme, and (3) a concise Lyapunov-based stability analysis demonstrating UUB of the closed-loop system.
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- © The Author(s), 2026. Published by Cambridge University Press
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This article has been updated since original publication. A notice detailing the change has been published.
References
Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A. and Hogan, R., “New talent signals: Shiny new objects or a brave new world,” Industr. Organ. Psychol. 9(03), 621–640 (2016).CrossRefGoogle Scholar
Tamilselvan, S. N. and Shanmugan, S., “Towards sustainable solar cells: Unveiling the latest developments in bio-nano materials for enhanced DSSC efficiency,” Clean Energy 8(3), 238–257 (2024).CrossRefGoogle Scholar
Ahmad, T., Madonski, R., Zhang, D., Huang, C. and Mujeeb, A., “Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm,” Renew. Sustain. Energy Rev. 160, 112128 (2022).CrossRefGoogle Scholar
Cebollada, S., Payá, L., Flores, M., Peidró, A. and Reinoso, O., “A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data,” Expert Syst. Appl. 167, 114195 (2021).CrossRefGoogle Scholar
Jimbo, K., “Material extrusion-based additive manufacturing with multi-legged robot: Fundamental investigation of path planning and manufacturing capabilities,” J. Adv. Simul. Sci. Eng. 11(1), 222–235 (2024).Google Scholar
Hernando, M., Alonso, M., Prados, C. and Gambao, E., “Behavior-based control architecture for legged-and-climber robots,” Appl. Sci. 11(20), 9547 (2021).CrossRefGoogle Scholar
Zhan, X., Chen, Y., Yang, Y., Hu, M., Chen, Z. and Wen, C.. “Multi-UAV Path Planning for Persistent Monitoring Under Uncertain Flight Time,” In: 2024 43rd Chinese Control Conference (CCC) (IEEE, 2024) pp. 4529–4534.Google Scholar
Smith, J., Doe, R. and Brown, T., “Hybrid disturbance rejection in multi-legged robots using adaptive impedance control,” IEEE Trans. Robot. 38(2), 512–525 (2022).Google Scholar
Lee, H. and Wang, K., “Static-dynamic transition stability for hexapods on uneven terrain: A Lyapunov function approach,” Robotica 39(4), 721–735 (2021).Google Scholar
Zhang, R., Liu, Y. and Chen, W., “Cockroach-inspired distributed posture control under vibrational disturbances,” Bioinspir. Biomim. 18(4), 045001 (2023).Google Scholar
Chen, L., Yang, G. and Patel, V., “Real-time dynamic gait optimization for hexapod robots using reinforcement learning,” Auton. Robot. 44(6), 1023–1039 (2020).Google Scholar
Gonzalez, F., Zhang, P. and Gupta, M., “Field robustness evaluation of bio-inspired robots in unstructured environments,” J. Field Robot. 38(6), 891–912 (2021).Google Scholar
Patel, S., Johnson, A. and Martínez, S., “Hybrid disturbance modeling for robotic systems: A compound Poisson process framework,” Int. J. Robot. Res. 41(5), 623–641 (2022).Google Scholar
Chen, Y., Grezmak, J. E., Graf, N. M. and Daltorio, K. A., “Sideways crab-walking is faster and more efficient than forward walking for a hexapod robot,” Bioinspir. Biomim. 17(4), 046001 (2022).CrossRefGoogle ScholarPubMed
Chen, G., Han, Y., Li, Y., Shen, J., Tu, J., Yu, Z., Zhang, J., Cheng, H., Zhu, L. and Dong, F., “Autonomous gait switching method and experiments of a hexapod walking robot for Mars environment with multiple terrains,” Intell. Serv. Robot. 1(21), (2024).Google Scholar
Xu, X., Qi, C., Han, L., Chen, Z., Gao, F., Wang, W., Chen, M. and Chen, X., “Multiple terrain traversal capabilities based mechanism dimension design for a six-legged robot using performance charts from analytical conditions,” Mech. Mach. Theory 201, 105731 (2024).CrossRefGoogle Scholar
Čížek, P., Zoula, M. and Faigl, J., “Design, construction, and rough-terrain locomotion control of novel hexapod walking robot with four degrees of freedom per leg,” IEEE Access 9, 17866–17881 (2021).CrossRefGoogle Scholar
Wu, F., Tang, W., Zhou, Y., Lin, S.-W., Ding, Z. and Liu, Y., “Robust motion planning for mobile robots under attacks against obstacle localization,” Robotica 1(20), (2024).Google Scholar
Wilson, S. and Ijspeert, A., “Central pattern generators for rhythmic gait generation in hexapod robots: A review,” Front. Robot. AI 7, 78 (2020).Google Scholar
Kim, D., Park, J. and Kim, S., “A tilting compensation framework for legged robots using inertial-kinematic sensor fusion,” IEEE/ASME Trans. Mechatron. 28(1), 432–443 (2023).Google Scholar
Brown, M., Thomas, G. and Anderson, K., “Kalman-filter-based sensor fusion for real-time disturbance classification in mobile robots,” Robot Auton. Syst. 136, 103702 (2021).Google Scholar
Zhao, Y., Wang, H. and Lewis, F., “Hierarchical control architecture for multi-agent robotic systems under disturbances,” Sci. Robot. 7(65), eabm6076 (2022).Google Scholar
Wang, X., Chen, Z. and Li, Y., “Lyapunov-based stability guarantees for legged robots with hybrid disturbances,” Automatica 148, 110783 (2023).Google Scholar
Zhang, C., Zou, W., Ma, L. and Wang, Z., “Biologically inspired jumping robots: A comprehensive review,” Robot. Auton. Syst. 124, 103362 (2020).CrossRefGoogle Scholar
Liu, Z., Huang, Z. and Cui, Y., “Gait planning based on bionic quadruped robot,” Eng. Res. Express 6(3), 035231 (2024).CrossRefGoogle Scholar
Chatzisavvas, A., Chatzitoulousis, P., Ziouzios, D. and Dasygenis, M., “A routing and task-allocation algorithm for robotic groups in warehouse environments,” Information 13(6), 288 (2022).CrossRefGoogle Scholar
Chen, C., Zha, F., Guo, W., Li, Z., Sun, L. and Shi, J., “Trajectory adaptation of biomimetic equilibrium point for stable locomotion of a large-size hexapod robot,” Auton. Robot. 45, 155–174 (2021).CrossRefGoogle Scholar
Batista, J. G., Ramalho, G. L. B., Torres, M. A., Oliveira, A. L. and Ferreira, D. S., “Collision avoidance for a selective compliance assembly robot arm manipulator using topological path planning,” Appl. Sci. 13(21), 11642 (2023).CrossRefGoogle Scholar
Gonçalves, F., Ribeiro, T., Ribeiro, A. F., Lopes, G. and Flores, P., “A recursive algorithm for the forward kinematic analysis of robotic systems using euler angles,” Robotics 11(1), 15 (2022).CrossRefGoogle Scholar
Saranli, U., Buehler, M. and Koditschek, D. E., “RHex: A simple and highly mobile hexapod robot,” Int. J. Robot. Res. 20(7), 616–631 (2001).CrossRefGoogle Scholar
Soliman, A., Ribeiro, G. A., Gan, D. and Rastgaar, M., “Feasibility design and control of a lower leg gait emulator utilizing a mobile 3-revolute, prismatic, revolute parallel manipulator,” J. Mech. Robot. 15(1), 014502 (2023).CrossRefGoogle Scholar
Liang, Y. P. and Chou, L. S., “Assessment of gait balance control using inertial measurement units – A narrative review,” World Sci. Ann. Rev. Biomech. 1, 2330006 (2023).Google Scholar
Zhang, Y., Liu, W. and Tan, N., “Motion control of legged robots based on gradient central pattern generators,” Robotica 1(30), 3102–3131 (2024).CrossRefGoogle Scholar
Chen, J.-P., San, H.-J., Wu, X. and Xiong, B.-Z., “Structural design and gait research of a new bionic quadruped robot,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. 236(14), 1912–1922 (2022).CrossRefGoogle Scholar
Kapsalyamov, A., Hussain, S., Goecke, R., Brown, N. A. T. and Jamwal, P. K., “Customized stiffness control strategy for a six-bar linkage-based gait rehabilitation robot,” Robotica 1(18), (2024).Google Scholar
Kalra, T., Singla, P. and Saini, R., “Time-synchronized gait transitions in embedded hexapod controllers,” Int. J. Robot. Res. 42(1), 65–82 (2023).Google Scholar
Martinez, L. and Tsai, K., “Servo coordination and real-time gait control for multi-legged robots,” IEEE Trans. Ind. Electron. 69(11), 11540–11550 (2022).Google Scholar
Wu, J., Ma, H. and Chen, K., “Adaptive dual-threshold-based pose recognition for mobile robots in noisy environments,” Robot. Auton. Syst. 167, 104448 (2023).Google Scholar
Gao, H., Liu, Y. and Zhang, Z., “Hierarchical disturbance-resilient control for legged robots in embedded systems,” IEEE Trans. Control Syst. Technol. 31(5), 2031–2043 (2023).Google Scholar
Inoue, K., Yamada, M. and Tokuda, H., “Tilted platform-based closed-loop testing of balance controllers for hexapod robots,” IEEE/ASME Trans. Mechatron. 27(3), 1412–1423 (2022).Google Scholar
Coelho, J., Dias, B., Lopes, G., Ribeiro, F. and Flores, P., “Development and implementation of a new approach for posture control of a hexapod robot to walk in irregular terrains,” Robotica 42(3), 792–816 (2024).CrossRefGoogle Scholar
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