CONTRIBUTIONS TO THE MODELING AND SIMULATION OF A BIPED WALKING ROBOT
Keywords:
bipedal robot, walking robot, mechanical structure, foot joints
Abstract
This study presents the modeling and simulation of a biped walking robot using MathWorks® simulation tools. The project focuses on developing a parametric and dynamic model that accurately represents the mechanical structure, joint kinematics, and actuator behavior of a bipedal robot. The methodology involves 3D modeling of the robot’s components, simulation of foot–ground contact forces, and comparative analysis of different actuation strategies—motion-based, torque-driven, and motor-based systems. By integrating mechanical, electrical, and control subsystems, the study evaluates trade-offs between model fidelity and simulation speed. Results indicate that while motor-actuated models provide higher physical realism, they significantly increase computational complexity. The work also highlights the importance of appropriate damping, force modeling, and control architecture (PID) for ensuring stable gait dynamics. Future developments include hardware implementation, enhanced actuator modeling, and machine learning approaches for optimizing walking performance.
References
1. Chen, X., & Zhang, Y. (2025). Modeling and Simulation of a Bipedal Robot with Series Elastic Actuators: A Port-Hamiltonian Approach in MATLAB. Mechanism and Machine Theory, 154, 104-120.
2. Wills, A., Schön, T. B., Ljung, L. & Ninness, B. (2013). Identification of Hammerstein–Wiener Models. Automatica, 49(1), 70-81.
3. Patel, R., & Jones, D. (2024). Data-Driven System Identification of a Bipedal Robot using MATLAB's System Identification Toolbox. Control Engineering Practice, 132, 105-118.
This study used MATLAB's System
4. Anderson, B., & Taylor, C. (2022). Simulation of Human-Like Bipedal Walking using a MATLAB-Based Neuromuscular Model. Journal of Biomechanics, 134, 110-125.
5. Singh, A., et al. (2024). Co-Simulation of Electromechanical Actuators for a High-Performance Bipedal Robot using Simscape. Mechatronics, 88, 102-115.
6. Harris, L., & Clark, K. (2024). Parameter Identification and Sensitivity Analysis of a Bipedal Robot Model using Global Optimization in MATLAB. Engineering Applications of Artificial Intelligence, 126, 107-119.
7. Wright, S., & Adams, J. (2024). Simulation of Bipedal Locomotion on Deformable Terrain using a Co-Simulation between MATLAB and a Discrete Element Method. Granular Matter, 26(2), 1-15.
8. Nguyen, T., et al. (2023). Model Predictive Control for Bipedal Locomotion: A MATLAB/Simulink Framework for Real-Time Simulation. IEEE Access, 11, 45672-45685..
9. Kumar, S., & Park, H. (2023). Real-Time Gait Phase Estimation and Adaptive Control for a Powered Ankle-Foot Prosthesis using MATLAB/Simulink. IEEE/ASME Transactions on Mechatronics, 28(1), 210-222.
10. Wang, J., et al. (2023). Hybrid Zero Dynamics Control for a 3D Bipedal Robot: Simulation and Implementation using Simscape Multibody. International Journal of Humanoid Robotics, 20(04), 225-245.
11. Baker, N., & Hill, G. (2023). Robust H-Infinity Control for a Bipedal Robot using MATLAB's Robust Control Toolbox. International Journal of Control, Automation and Systems, 21(3), 889-901.
12. Martinez, C., & Davis, P. (2023). Fault-Tolerant Control of a Bipedal Robot under Actuator Failure: A Simulation Study in MATLAB/Simulink. Annual Reviews in Control, 55, 321-335.
13. Perez, M., & Scott, T. (2023). Energy-Based Control and Simulation of Passive Dynamic Walkers in MATLAB. Nonlinear Dynamics, 111(2), 1457-1475.
14. Garcia, M., et al. (2022). A MATLAB-Based Optimization Toolbox for Generating Dynamic Bipedal Walking Gaits. Robotics and Autonomous Systems, 148, 103-115.
15. Li, W., et al. (2024). A Deep Reinforcement Learning Framework for Robust Bipedal Locomotion on Irregular Terrain using MATLAB and Simulink. IEEE Transactions on Robotics, 40(2), 501-517.
16. Evans, R., & Green, M. (2025). Generative Adversarial Networks for Bipedal Gait Synthesis: Training and Evaluation in a MATLAB Environment. Nature Machine Intelligence, 7(1), 45-58.
17. Rodriguez, F., & King, E. (2022). From Simulation to Reality: A MATLAB Workflow for Validating Bipedal Robot Designs. Journal of Field Robotics, 39(5), 678-695.
18. Thompson, J., & White, S. (2022). A MATLAB-Based Framework for Multi-Robot Bipedal Locomotion and Collaboration. Robotics and Computer-Integrated Manufacturing, 74, 102-118.
19. Kim, H., & Brown, D. (2025). Leveraging MATLAB's Computer Vision Toolbox for Vision-Based Bipedal Locomotion over Obstacles. IEEE International Conference on Robotics and Automation (ICRA), 1-8.
20. Morris, A., & Turner, B. (2025). A Digital Twin for a Bipedal Service Robot: Real-Time Simulation and Monitoring with MATLAB. Advanced Engineering Informatics, 55, 101-115.
21. Lee, K., & Park, S. (2022). A Comparative Study of ODE Solvers in MATLAB for Simulating Bipedal Robot Dynamics. Journal of Computational and Nonlinear Dynamics, 17(3), 031-045.
22. Mathworks® - technical documentation
23. Cantrell AJ, Imonugo O, Varacallo MA. (2023) Anatomy, Bony Pelvis and Lower Limb: Leg Bones. StatPearls [Internet]. Treasure Island (FL)
24. Wills, A., Schön, T. B., Ljung, L. & Ninness, B. (2013). Identification of Hammerstein–Wiener Models. Automatica, 49(1), 70-81.
25. A. van der Schaft and D. Jeltsema (2014). Port-Hamiltonian Systems Theory: An Introductory
Overview. Foundations and Trends® in Systems and Control, vol. 1, no. 2-3, 173-37
2. Wills, A., Schön, T. B., Ljung, L. & Ninness, B. (2013). Identification of Hammerstein–Wiener Models. Automatica, 49(1), 70-81.
3. Patel, R., & Jones, D. (2024). Data-Driven System Identification of a Bipedal Robot using MATLAB's System Identification Toolbox. Control Engineering Practice, 132, 105-118.
This study used MATLAB's System
4. Anderson, B., & Taylor, C. (2022). Simulation of Human-Like Bipedal Walking using a MATLAB-Based Neuromuscular Model. Journal of Biomechanics, 134, 110-125.
5. Singh, A., et al. (2024). Co-Simulation of Electromechanical Actuators for a High-Performance Bipedal Robot using Simscape. Mechatronics, 88, 102-115.
6. Harris, L., & Clark, K. (2024). Parameter Identification and Sensitivity Analysis of a Bipedal Robot Model using Global Optimization in MATLAB. Engineering Applications of Artificial Intelligence, 126, 107-119.
7. Wright, S., & Adams, J. (2024). Simulation of Bipedal Locomotion on Deformable Terrain using a Co-Simulation between MATLAB and a Discrete Element Method. Granular Matter, 26(2), 1-15.
8. Nguyen, T., et al. (2023). Model Predictive Control for Bipedal Locomotion: A MATLAB/Simulink Framework for Real-Time Simulation. IEEE Access, 11, 45672-45685..
9. Kumar, S., & Park, H. (2023). Real-Time Gait Phase Estimation and Adaptive Control for a Powered Ankle-Foot Prosthesis using MATLAB/Simulink. IEEE/ASME Transactions on Mechatronics, 28(1), 210-222.
10. Wang, J., et al. (2023). Hybrid Zero Dynamics Control for a 3D Bipedal Robot: Simulation and Implementation using Simscape Multibody. International Journal of Humanoid Robotics, 20(04), 225-245.
11. Baker, N., & Hill, G. (2023). Robust H-Infinity Control for a Bipedal Robot using MATLAB's Robust Control Toolbox. International Journal of Control, Automation and Systems, 21(3), 889-901.
12. Martinez, C., & Davis, P. (2023). Fault-Tolerant Control of a Bipedal Robot under Actuator Failure: A Simulation Study in MATLAB/Simulink. Annual Reviews in Control, 55, 321-335.
13. Perez, M., & Scott, T. (2023). Energy-Based Control and Simulation of Passive Dynamic Walkers in MATLAB. Nonlinear Dynamics, 111(2), 1457-1475.
14. Garcia, M., et al. (2022). A MATLAB-Based Optimization Toolbox for Generating Dynamic Bipedal Walking Gaits. Robotics and Autonomous Systems, 148, 103-115.
15. Li, W., et al. (2024). A Deep Reinforcement Learning Framework for Robust Bipedal Locomotion on Irregular Terrain using MATLAB and Simulink. IEEE Transactions on Robotics, 40(2), 501-517.
16. Evans, R., & Green, M. (2025). Generative Adversarial Networks for Bipedal Gait Synthesis: Training and Evaluation in a MATLAB Environment. Nature Machine Intelligence, 7(1), 45-58.
17. Rodriguez, F., & King, E. (2022). From Simulation to Reality: A MATLAB Workflow for Validating Bipedal Robot Designs. Journal of Field Robotics, 39(5), 678-695.
18. Thompson, J., & White, S. (2022). A MATLAB-Based Framework for Multi-Robot Bipedal Locomotion and Collaboration. Robotics and Computer-Integrated Manufacturing, 74, 102-118.
19. Kim, H., & Brown, D. (2025). Leveraging MATLAB's Computer Vision Toolbox for Vision-Based Bipedal Locomotion over Obstacles. IEEE International Conference on Robotics and Automation (ICRA), 1-8.
20. Morris, A., & Turner, B. (2025). A Digital Twin for a Bipedal Service Robot: Real-Time Simulation and Monitoring with MATLAB. Advanced Engineering Informatics, 55, 101-115.
21. Lee, K., & Park, S. (2022). A Comparative Study of ODE Solvers in MATLAB for Simulating Bipedal Robot Dynamics. Journal of Computational and Nonlinear Dynamics, 17(3), 031-045.
22. Mathworks® - technical documentation
23. Cantrell AJ, Imonugo O, Varacallo MA. (2023) Anatomy, Bony Pelvis and Lower Limb: Leg Bones. StatPearls [Internet]. Treasure Island (FL)
24. Wills, A., Schön, T. B., Ljung, L. & Ninness, B. (2013). Identification of Hammerstein–Wiener Models. Automatica, 49(1), 70-81.
25. A. van der Schaft and D. Jeltsema (2014). Port-Hamiltonian Systems Theory: An Introductory
Overview. Foundations and Trends® in Systems and Control, vol. 1, no. 2-3, 173-37
Published
2025-12-31
How to Cite
Popescu, D., Ungureanu, L., & Amza, C. (2025). CONTRIBUTIONS TO THE MODELING AND SIMULATION OF A BIPED WALKING ROBOT. Nonconventional Technologies Review, 29(4). Retrieved from http://www.revtn.ro/index.php/revtn/article/view/556
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Articles
