Hostname: page-component-848d4c4894-v5vhk Total loading time: 0 Render date: 2024-06-26T01:41:58.318Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancement of humanoid robot locomotion on slippery floors using an adaptive controller

Published online by Cambridge University Press:  17 January 2024

Luís Almeida Open the ORCID record for Luís Almeida [Opens in a new window] ,
Vítor Santos Open the ORCID record for Vítor Santos [Opens in a new window]  and
João Ferreira Open the ORCID record for João Ferreira [Opens in a new window]
Luís Almeida*
Affiliation:
Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Aveiro University, Aveiro, 3810-193, Portugal
Vítor Santos
Affiliation:
Institute of Electronics and Informatics Engineering of Aveiro (IEETA), Aveiro University, Aveiro, 3810-193, Portugal Department of Mechanical Engineering, Aveiro University, Aveiro, 3810-193, Portugal
João Ferreira
Affiliation:
Department of Electrical Engineering, Superior Institute of Engineering of Coimbra, Coimbra, 3030-199, Portugal Institute of Systems and Robotics (ISR), Universidade de Coimbra, Coimbra, 3030-290, Portugal
*
Corresponding author: Luís Almeida; Email: lmalmeida@ua.pt
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm. The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption. The study has practical implications for designing more versatile and effective solutions for humanoid locomotion on challenging surfaces and highlights the adaptability of the existing controller for different humanoid robots.

Keywords

adaptive controlPID controllerhumanoid robotdata processing
Type
Research Article
Information
Robotica , Volume 42 , Issue 4 , April 2024 , pp. 1055 - 1073
Copyright
© The Author(s), 2024. 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

Ding, J., Wang, Y., Yang, M. and Xiao, X., “Walking stabilization control for humanoid robots on unknown slope based on walking sequences adjustment,” J. Intell. Robot. Syst. 90(3), 323–338 (2018). doi: 10.1007/s10846-017-0685-4.CrossRefGoogle Scholar
Kim, J.-H., Kim, J.-Y. and Oh, J.-H., “Adjustment of home posture of biped humanoid robot using sensory feedback control,” J. Intell. Robot. Syst. 51(4), 421–438 (2008). doi: 10.1007/s10846-007-9195-0.CrossRefGoogle Scholar
Almeida, L., Santos, V. and Ferreira, J. P., “Learning-based analysis of a new wearable 3D force system data to classify the underlying surface of a walking robot,” Int. J. HR 17(03), 2050011 (2020). doi: 10.1142/S0219843620500115.Google Scholar
Almeida, L., Santos, V. and Ferreira, J. P., “Real-time LSTM-RNN classification of floors with different friction coefficients for a walking humanoid robot wearing a 3D force system,” IEEE Sens. J. 21(24), 27801–27809 (2021). doi: 10.1109/JSEN.2021.3124854.CrossRefGoogle Scholar
Vukobratović, M. and Stepanenko, J., “On the stability of anthropomorphic systems,” Math. Biosci. 15(1), 1–37 (1972). doi: 10.1016/0025-5564(72)90061-2.CrossRefGoogle Scholar
Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K. and Hirukawa, H., “The 3D Linear Inverted Pendulum Mode: A Simple Modeling for a Biped Walking Pattern Generation,” In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the Next Millennium (Cat. No.01CH37180) (2001). doi: 10.1109/IROS.2001.973365.Google Scholar
Englsberger, J., Ott, C. and Albu-Schäffer, A. O., “Three-Dimensional Bipedal Walking Control Using Divergent Component of Motion,” In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (2013). doi: 10.1109/TRO.2015.2405592.Google Scholar
Koolen, T., de Boer, T., Rebula, J., Goswami, A. and Pratt, J. E., “Capturability-based analysis and control of legged locomotion, part 1: Theory and application on three simple gait models,” Int. J. Robot. Res. 31(9), 1094–1113 (2012). doi: 10.1177/0278364912452673.CrossRefGoogle Scholar
Xie, Y., Gao, C., Zhu, S., Yan, X., Kong, L., Xie, A., Gu, J. and Zhang, D., “Gait optimization and energy-based stability for biped locomotion using large-scale programming,” Robotica 41(7), 2207–2227 (2023). doi: 10.1017/S0263574723000413.CrossRefGoogle Scholar
Jalaeian-F, M., Fateh, M. and Rahimiyan, M., “Bi-level adaptive computed-current impedance controller for electrically driven robots,” Robotica 39(2), 200–216 (2021). doi: 10.1017/S0263574720000314.CrossRefGoogle Scholar
Murooka, M., Chappellet, K., Tanguy, A., Benallegue, M., Kumagai, I., Morisawa, M., Kanehiro, F. and Kheddar, A., “Humanoid loco-manipulations pattern generation and stabilization control,” IEEE Robot. Autom. Lett. 6(3), 5597–5604 (2021). doi: 10.1109/LRA.2021.3077858.CrossRefGoogle Scholar
Mesesan, G., Englsberger, J., Ott, C. and Albu-Schäffer, A., “Convex properties of center-of-mass trajectories for locomotion based on divergent component of motion,” IEEE Robot. Autom. Lett. 3(4), 3449–3456 (2018). doi: 10.1109/LRA.2018.2853557.CrossRefGoogle Scholar
Shafiee, M., Romualdi, G., Dafarra, S., Chavez, F. J. A. and Pucci, D., “Online DCM Trajectory Generation for Push Recovery of Torque-Controlled Humanoid Robots,” In: 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids) (2019) pp. 671–678. doi: 10.1109/Humanoids43949.2019.9034996.CrossRefGoogle Scholar
Romualdi, G., Dafarra, S., Hu, Y., Ramadoss, P., Chavez, F. J. A., Traversaro, S. and Pucci, D., “A benchmarking of DCM-based architectures for position, velocity and torque-controlled humanoid robots,” Int. J. HR 17(01), 1950034 (2020). doi: 10.1142/S0219843619500348.Google Scholar
Griffin, R., McCrory, S., Bertrand, S., Calvert, D., Lee, I., Neuhaus, P., Stephen, D., Jasper, J., Karumanchi, S., Kourchians, A., Emanuel, B., Holmes, E., Hegeman, R., Pusey, J. and Pratt, J., “Quadrupedal walking over complex terrain with a quasi-direct drive actuated robot,” Field Robot. 2(1), 356–384 (2022). doi: 10.55417/fr.2022013.CrossRefGoogle Scholar
Wagner, M., Applying the Divergent Component of Motion Method for Quadrupedal Locomotion to a Robot with Series Elastics Actuators. Ph.D. thesis (DLR-RM, 2021).Google Scholar
Mehr, J. K., Sharifi, M., Mushahwar, V. K. and Tavakoli, M., “Intelligent locomotion planning with enhanced postural stability for lower-limb exoskeletons,” IEEE Robot. Autom. Lett. 6(4), 7588–7595 (2021). doi: 10.1109/LRA.2021.3098915.CrossRefGoogle Scholar
Xu, D., Huang, P., Li, Z. and Feng, Y., “DMP-Based Motion Generation for a Walking Exoskeleton Robot Using Divergent Component of Motion,” In: 2022 International Conference on Advanced Robotics and Mechatronics (ICARM) (2022) pp. 232–237. doi: 10.1109/ICARM54641.2022.9959591.CrossRefGoogle Scholar
Romualdi, G., Dafarra, S., Hu, Y. and Pucci, D., “A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots,” In: IEEE-RAS 18th International Conference on Humanoid Robots (2018) pp. 1–9. doi: 10.1109/HUMANOIDS.2018.8625025.Google Scholar
Choi, Y., Kim, D., Oh, Y. and You, B.-J., “Posture/walking control for humanoid robot based on kinematic resolution of CoM Jacobian with embedded motion,” IEEE Trans. Robot. 23(6), 1285–1293 (2007). doi: 10.1109/TRO.2007.904907.CrossRefGoogle Scholar
Herdt, A., Diedam, H., Wieber, P.-B., Dimitrov, D., Mombaur, K. D. and Diehl, M., “Online walking motion generation with automatic footstep placement,” Adv. Robot. 24(5–6), 719–737 (2010). doi: 10.1163/016918610X493552.CrossRefGoogle Scholar
Zhou, Q., Yu, Z., Zhang, S., Chen, X., Qin, M., Zhang, W. and Huang, Q., “Simultaneous prevention of rotational and translational slip for a humanoid robot,” Appl. Sci. 8(9), 1554 (2018). doi: 10.3390/app8091554.CrossRefGoogle Scholar
Brandão, M., Hashimoto, K., Santos-Victor, J. and Takanishi, A., “Gait Planning for Biped Locomotion on Slippery Terrain,” In: 2014 IEEE-RAS International Conference on Humanoid Robots, Madrid, Spain (2014) pp. 303–308. doi: 10.1109/HUMANOIDS.2014.7041376.CrossRefGoogle Scholar
Nguyen, X. T., Nguyen, D. H., Nguyen, H. H., Tong, N. P., Nguyen, T. P. and Nguyen, T. T., “Balancing Walking Gait for Small Size Humanoid Robot by Using Movable Mass,” In: 2020 International Conference on Advanced Mechatronic Systems (ICAMechS) (2020) pp. 13–16. doi: 10.1109/ICAMechS49982.2020.9310109.CrossRefGoogle Scholar
Bestmann, M. and Zhang, J., “Bipedal Walking on Humanoid Robots through Parameter Optimization,” In: RoboCup Symposium (2022). doi: 10.1007/978-3-031-28469.Google Scholar
Xu, Z., Li, R., Zhong, B. and Peng, Y., “Design of a humanoid dance robot for dancing Baduanjin,” Proceedings International Conference of Artificial Life and Robotics, vol. 27 (2022), pp. 938–945. doi: 10.5954/icarob.2022.os9-8.CrossRefGoogle Scholar
Kajita, S., Kanehiro, F., Kaneko, K., Yokoi, K. and Hirukawa, H., “The 3D Linear Inverted Pendulum Mode: A Simple Modeling for a Biped Walking Pattern Generation,” In: Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the Next Millennium (Cat. No. 01CH37180), vol. 1 (2001) pp. 239–246. doi: 10.1109/IROS.2001.973365.Google Scholar
Almeida, L., Santos, V. and Silva, F., “A Novel Wireless Instrumented Shoe for Ground Reaction Forces Analysis in Humanoids,” In: 2018 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (2018) pp. 36–41. doi: 10.1109/ICARSC.2018.8374157.CrossRefGoogle Scholar
Lee, Y. H., Lee, Y. H., Lee, H., Kang, H., Lee, J. H., Park, J. M., Kim, Y. B., Moon, H., Koo, J. C. and Choi, H. R., “Whole-body control and angular momentum regulation using torque sensors for quadrupedal robots,” J. Intell. Robot. Syst. 102(3), 66 (2021). doi: 10.1007/s10846-021-01418-x.CrossRefGoogle Scholar
Åström, K. J. and Hägglund, T., “The future of PID control,” Control Eng. Pract. 9(11), 1163–1175 (2001). doi: 10.1016/S0967-0661(01)00062-4.CrossRefGoogle Scholar
Julier, S. J. and Uhlmann, J. K., “New Extension of the Kalman Filter to Nonlinear Systems,” In: Signal Processing, Sensor Fusion, and Target Recognition VI (Kadar, I., ed.), vol. 3068 (SPIE, 1997). doi: 10.1117/12.280797.Google Scholar
Aller, F., Pinto-Fernandez, D., Torricelli, D., Pons, J. L. and Mombaur, K., “From the state of the art of assessment metrics toward novel concepts for humanoid robot locomotion benchmarking,” IEEE Robot. Autom. Lett. 5(2), 914–920 (2019). doi: 10.1109/LRA.2019.2952291.CrossRefGoogle Scholar