Nonlinear Model Predictive Control for Pose Regulation Robot and Obstacle Avoidance

https://doi.org/10.58571/CNCA.AMCA.2024.042

Resumen: The present work addressed the problem of autonomous navigation of wheeled mobile robots, specifically the Differential Driving Robot (DDR). The DDR kinematic model is nonlinear, which requires adequate automatic control strategies for good performance in autonomous navigation. The variables of the DDR mathematical model are the robot’s position expressed in a global Cartesian reference frame, its orientation, its linear speed, and its angular speed. It was proposed that the navigation problem of a DDR be solved by following a reference trajectory using model-based nonlinear predictive control. In addition, a potential field algorithm was added for obstacle avoidance. Experiments were carried out in a dynamic simulator. Several simulations provided convincing evidence of the feasibility of implementing a real robot using the proposed approach.

¿Cómo citar?

Rocha, A.R., Aldana Murillo, N.G., Martinez, E. & Ovalle Magallanes, E. (2024). Nonlinear Model Predictive Control for Pose Regulation Robot and Obstacle Avoidance. Memorias del Congreso Nacional de Control Automático 2024, pp. 244-249. https://doi.org/10.58571/CNCA.AMCA.2024.042

Palabras clave

Autonomous Navigation, Differential Drive Robot, Nonlinear Model Predictive Control, Obstacle Avoidance

• Andersson, J.A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (2019). CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1), 1–36. doi:10.1007/s12532-018-0139-4.
• Bicchi, A., Casalino, G., and Santilli, C. (1995). Planning shortest bounded-curvature paths for a class of nonholonomic vehicles among obstacles. In IEEE International Conference on Robotics and Automation, volume 2, 1349–1354 vol.2. doi:10.1109/ROBOT.1995. 525466.
• Borenstein, J. and Koren, Y. (1989). Real-time obstacle avoidance for fast mobile robots. IEEE Transactions on Systems, Man, and Cybernetics, 19(5), 1179–1187. doi:10.1109/21.44033.
• Bouzoualegh, S., Guechi, E.H., and Kelaiaia, R. (2018). Model predictive control of a differential-drive mobile robot. Acta Universitatis Sapientiae, Electrical and Mechanical Engineering, 10(1), 20–41. doi:doi:10.2478/auseme-2018-0002.
• Chawengkrittayanont, P., Pukdeboon, C., Kuntanapreeda, S., and Moore, E.J. (2021). Smooth second-order sliding mode controller for multivariable mechanical systems. SN Applied Sciences, 3, 1–18.
• Hayet, J.B., Carlos, H., Esteves, C., and Murrieta-Cid, R. (2014). Motion planning for maintaining landmarks visibility with a differential drive robot. Robotics and Autonomous Systems, 62(4), 456-473. doi:https://doi.org/10.1016/j.robot.2013.12.003.
• Hedjar, R. (2022). Approximate quadratic programming algorithm for nonlinear model predictive tracking control of a wheeled mobile robot. IEEE Access, 10, 65067–65079. doi:10.1109/ACCESS.2022.3178727.
• Jun, C. and Lin, W. (2020). Track tracking of double joint robot based on sliding mode control. In IEEE International Conference on Information Systems and Computer Aided Education, 626–631. doi:10.1109/ICISCAE51034.2020.9236895.
• Khatib, O. (1985). Real-time obstacle avoidance for manipulators and mobile robots. In IEEE International Conference on Robotics and Automation, volume 2, 500–505. doi:10.1109/ROBOT.1985.1087247.
• Lages, W.F. and Vasconcelos Alves, J.A. (2006). Realtime control of a mobile robot using linearized model predictive control. IFAC Proceedings Volumes, 39(16), 968–973. doi:https://doi.org/10.3182/20060912-3-DE-2911.00166.
• Li, J., Wang, J., Peng, H., Hu, Y., and Su, H. (2022). Fuzzy-torque approximation-enhanced sliding mode control for lateral stability of mobile robot. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(4), 2491–2500. doi:10.1109/TSMC.2021. 3050616.
• Martinez, E., Laguna, G., Murrieta-Cid, R., Becerra, H.M., Lopez-Padilla, R., and LaValle, S.M. (2019). A motion strategy for exploration driven by an automaton activating feedback-based controllers. Autonomous Robots, 43(7), 1801–1825. doi:10.1007/s10514-019-09835-6.
• Mehrez, M.W., Mann, G.K.I., and Gosine, R.G. (2015). Comparison of stabilizing nmpc designs for wheeled mobile robots: An experimental study. In Moratuwa Engineering Research Conference, 130–135. doi:10.1109/MERCon.2015.7112333.
• Minguez, J. and Montano, L. (2004). Nearness diagram (nd) navigation: collision avoidance in troublesome scenarios. IEEE Transactions on Robotics and Automation, 20(1), 45–59. doi:10.1109/TRA.2003. 820849.
• Sani, M., Robu, B., and Hably, A. (2021). Dynamic obstacles avoidance using nonlinear model predictive control. In 47th Annual Conference of the IEEE Industrial Electronics Society, 1–6. doi:10.1109/ IECON48115.2021.9589658.
• Shtessel, Y., Plestan, F., Edwards, C., and Levant, A. (2023). Adaptive sliding mode and higher order slidingmode control techniques with applications: A survey. In Sliding-Mode Control and Variable-Structure Systems: The State of the Art, 267–305. Springer.
• Thi, L.T., Quoc, H.P., Xuan, T.N., Dang, T.N., Quy, T.D., and Minh, D.D. (2023). Adaptive sliding mode control for series elastic actuator robots.
• In International Conference on Engineering Research and Applications, 159–168. Springer.
• Zeng, H. and Di, Y. (2022). Trajectory tracking control of mobile robot based on interval type ii fuzzy sliding mode. In IEEE International Conference on Electronic Technology, Communication and Information, 1420-1425. doi:10.1109/ICETCI55101.2022.9832266.