Research on Motion Control Method of Wheel-Legged Robot in Unstructured Terrain Based on Improved Central Pattern Generator (CPG) and Biological Reflex Mechanism
Source abstract
Abstract
With the development of inspection robot control technology, wheel-legged robots are increasingly used in complex underground space inspection. To address low stability during obstacle crossing in unstructured terrains, a motion control strategy integrating an improved CPG algorithm and a biological reflex mechanism is proposed. It introduces an adaptive coupling matrix, augmented with the Lyapunov function, and vestibular/stumbling reflex models for real-time motion feedback. Simulink–Adams virtual prototypes and single-wheeled leg experiments (on the left front leg) were used to verify the system. Results show that the robot’s turning oscillation was ≤±0.00593 m, the 10° tilt maintained a stable center of mass at 10.2° with roll angle fluctuations ≤±5°, gully-crossing fluctuations ≤±0.01 m, and pitch recovery ≤2 s. The experiments aligned with the simulations, proving that the strategy effectively suppresses vertical vibrations, ensuring stable and high-precision inspection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Locomotion and Control · Soil Mechanics and Vehicle Dynamics · Soft Robotics and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Huainan 232001, China
Funding
Anhui Provincial Key Research and Development Plan
References (24)
Show source-provided references
1. Zhang, Y., Qiao, G., Wan, Q., Tian, L., and Liu, D. (2023). A Novel Double-Layered Central Pattern Generator-Based Motion Controller for the Hexapod Robot. Mathematics, 11.10.3390/math11030617 ↗
2. Matsuoka’s CPG With Desired Rhythmic Signals for Adaptive Walking of Humanoid Robots10.1109/TCYB.2018.2870145 ↗
3. A CPG-based gait planning and motion performance analysis for quadruped robot10.1108/IR-08-2021-0181 ↗
4. Multi-Layered CPG for Adaptive Walking of Quadruped Robots10.1007/s42235-018-0026-8 ↗
5. Auddy, S., Sven, M., and Stefan, W. (2019, January 19–22). Hierarchical control for bipedal locomotion using central pattern generators and neural networks. Proceedings of the 2019 Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics (ICDL—EpRob), Oslo, Norway.10.1109/DEVLRN.2019.8850683 ↗
6. Model predictive control of legged and humanoid robots: Models and algorithms10.1080/01691864.2023.2168134 ↗
7. CPG-Based Hierarchical Locomotion Control for Modular Quadrupedal Robots Using Deep Reinforcement Learning10.1109/LRA.2021.3092647 ↗
8. Huang, Y., Bing, Z., Walter, F., Rohregger, A., Zhang, Z., Huang, K., Morin, F.O., and Knoll, A. (2022, January 23–27). Enhanced quadruped locomotion of a rat robot based on the lateral flexion of a soft actuated spine. Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan.10.1109/IROS47612.2022.9981674 ↗
9. Locomotion gait control of snake robots based on a novel unified CPG network model composed of Hopf oscillators10.1016/j.robot.2024.104746 ↗
10. Spatial locomotion of a metameric earthworm-like robot: Generation and analysis of gaits10.1007/s11044-023-09943-3 ↗
11. Nonlinear Model Predictive Control With Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment10.1109/TMECH.2023.3312672 ↗
12. AI-CPG: Adaptive Imitated Central Pattern Generators for Bipedal Locomotion Learned Through Reinforced Reflex Neural Networks10.1109/LRA.2024.3388842 ↗
13. CPG-RL: Learning Central Pattern Generators for Quadruped Locomotion10.1109/LRA.2022.3218167 ↗
14. Chen, S., Zhang, B., Mueller, M.W., Rai, A., and Sreenath, K. (2023, January 12–14). Learning torque control for quadrupedal locomotion. Proceedings of the 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), Austin, TX, USA.10.1109/Humanoids57100.2023.10375154 ↗
15. Enhancing hexapod robot mobility on challenging terrains: Optimizing CPG-generated gait with reinforcement learning10.1016/j.neucom.2024.129328 ↗
16. Watanabe, T., Kubo, A., Tsunoda, K., Matsuba, T., Akatsuka, S., Noda, Y., Kioka, H., Izawa, J., Ishii, S., and Nakamura, Y. (2025). Hierarchical reinforcement learning with central pattern generator for enabling a quadruped robot simulator to walk on a variety of terrains. Sci. Rep., 15.10.1038/s41598-025-94163-2 ↗
17. CPG-fuzzy-based control of a cownose-ray-like fish robot10.1108/IR-02-2019-0029 ↗
18. Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion10.1109/LRA.2021.3108510 ↗
19. Modeling, stability and walking pattern generators of biped robots: A review10.1017/S0263574713001124 ↗
20. A tightly-coupled LIDAR-IMU SLAM method for quadruped robots10.1177/00202940231224593 ↗
21. Zhou, X., Zhang, Y., Qiu, Z., Shan, Z., Cai, S., and Bao, G. (2024). Locomotion control of a rigid-soft coupled snake robot in multiple environments. Biomim. Intell. Robot., 4.10.1016/j.birob.2024.100148 ↗
22. Hutter, M., Gehring, C., Jud, D., Lauber, A., Bellicoso, C.D., Tsounis, V., Hwangbo, J., Bodie, K., Fankhauser, P., and Bloesch, M. (2016, January 9–14). ANYmal—A highly mobile and dynamic quadrupedal robot. Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.10.1109/IROS.2016.7758092 ↗
23. Gait Planning and Stability Control of a Quadruped Robot10.1155/2016/9853070 ↗
24. Bio-inspired central pattern generator for development of Cartesian motor skills for a Quadruped Robot10.30574/wjarr.2021.12.1.0485 ↗