A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 13 Issue 10
Oct.  2026

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 18.3, Top 1 (SCI Q1)
    CiteScore: 28.2, Top 1% (Q1)
    Google Scholar h5-index: 95， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(10): 2355-2366
W. Yuan, M. Yao, K. Liu, Y.-H. Liu, Y. Liu, and C.-Y. Su, “Integrated motion planning and control with slip resistance for wheeled mobile robots in obstacle-rich environments,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 10, pp. 2355–2366, Oct. 2026. doi: 10.1109/JAS.2026.125957
Citation: W. Yuan, M. Yao, K. Liu, Y.-H. Liu, Y. Liu, and C.-Y. Su, “Integrated motion planning and control with slip resistance for wheeled mobile robots in obstacle-rich environments,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 10, pp. 2355–2366, Oct. 2026. doi: 10.1109/JAS.2026.125957
shu

Integrated Motion Planning and Control With Slip Resistance for Wheeled Mobile Robots in Obstacle-Rich Environments

doi: 10.1109/JAS.2026.125957
Funds:  This work was supported by the National Natural Science Foundation of China (62103111, 62203186), the JC STEM Lab of Robotics for Soft Materials of The University of Hong Kong, and The Hong Kong Jockey Club Charities Trust, and Jiangsu Province Youth Science and Technology Talent Support Project (JSTJ-2025-140)
More Information
    Abstract
  • Autonomous mobile robots operating in environments with multiple obstacles require enhanced flexibility, which can increase the likelihood of slippage between the wheels and the ground. Although extensive research has proposed collision-avoidance trajectory planning methods and anti-slip trajectory tracking controls, demonstrating effective anti-collision and anti-slip outcomes, these studies often fail to integrate motion planning and control strategies for comprehensive testing. Addressing this issue, this paper introduces an integrated motion planning and control strategy aimed at improving the slip resistance performance of wheeled mobile robots (WMRs) in obstacle-rich environments. In terms of motion planning, this study employs a slip-aware momentum potential field (MPF) approach, an artificial potential field (APF) strategy that uses momentum gradient descent (MGD) to plan safe, collision-avoidance trajectories. Compared to conventional APF methods, MPF not only safely avoids obstacles but also effectively suppresses oscillations, adapts to motion trends, and reduces slippage components, thus enhancing the safety of path planning. At the control level, the kinematic equations of motion for a nonholonomic mobile robot, considering steering and longitudinal slip, are described, and then a differential flatness-based error model is constructed using dynamic feedback linearization. Subsequently, a robust control strategy based on control Lyapunov function (CLF) is developed. This strategy computes the optimal control inputs corresponding to the cost function under uncertain slip disturbances, ensuring optimal slip resistance performance in unknown operational environments. Unlike conventional hierarchical schemes, the proposed framework establishes explicit, cycle-level coupling between the motion planner and the motion controller. Under synchronous operation, execution feedback is returned to the planning layer in every cycle to actively reshape the potential field, while the controller concurrently suppresses slip-induced errors at the execution level. Together, these mechanisms form a bidirectional planning-control loop specifically designed to enhance slip resistance. The effectiveness and superiority of the proposed integrated strategy have been extensively validated through experimental results.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    Z. Liu, X. Zuo, M. Zhou, B. Jia, and C. Xin, “Meal delivery routing problem with a hybrid fleet of riders and autonomous vehicles under dynamic environment,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 11642–11655, Jan. 2025. doi: 10.1109/TASE.2025.3534143
    [2]
    Z. Zhao, X. Li, S. Liu, M. Zhou, and X. Yang, “Multi-mobile-robot transport and production integrated system optimization,” IEEE Trans. Autom. Sci. Eng., vol. 22, pp. 7480–7491, 2025. doi: 10.1109/TASE.2024.3421889
    [3]
    K. Lochan, A. Khan, I. Elsayed, B. Suthar, L. Seneviratne, and I. Hussain, “Advancements in precision spraying of agricultural robots: A comprehensive review,” IEEE Access, vol. 12, pp. 129447–129483, Aug. 2024. doi: 10.1109/ACCESS.2024.3450904
    [4]
    A. Alkhder, L. Alqudaihi, A. Almadan, and S. Al-Walaie, “Deployment of mobile robotics for high voltage switchyards assets inspection: Advances and trends,” in Proc. IEEE Sustainable Power and Energy Conf., Kuching, Sarawak, Malaysia, 2024, pp. 590−594.
    [5]
    Y. Zhu, W. Z. Wan Hasan, H. R. H. Ramli, N. M. H. Norsahperi, M. S. M. Kassim, and Y. Yao, “Deep reinforcement learning of mobile robot navigation in dynamic environment: A review,” Sensors, vol. 25, no. 11, Art. no. 3394, May 2025. doi: 10.3390/s25113394
    [6]
    Y. Hu, W. Zhou, Y. Liu, M. Zeng, W. Ding, S. Li, G. Li, Z. Li, and A. Knoll, “Efficient online planning and robust optimal control for nonholonomic mobile robot in unstructured environments,” IEEE Trans. Emerg. Top. Comput. Intell., vol. 8, no. 5, pp. 3559–3575, Oct. 2024. doi: 10.1109/TETCI.2024.3424527
    [7]
    L. Svensson, M. Bujarbaruah, N. R. Kapania, and M. Törngren, “Adaptive trajectory planning and optimization at limits of handling,” in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, Macau, China, 2019, pp. 3942−3948.
    [8]
    L. Zhu, Y. He, and Z. Liu, “Trajectory planning for intelligent vehicle obstacle avoidance in rainy and snowy conditions,” Proc. Inst. Mech. Eng. Part D: J. Automob. Eng., vol. 239, no. 12, pp. 5397–5411, Oct. 2025. doi: 10.1177/09544070241287819
    [9]
    S. J. Yoo, “Adaptive tracking and obstacle avoidance for a class of mobile robots in the presence of unknown skidding and slipping,” IET Control Theory Appl., vol. 5, no. 14, pp. 1597–1608, Sep. 2011. doi: 10.1049/iet-cta.2010.0644
    [10]
    L. Ögretmen, M. Rowold, M. Ochsenius, and B. Lohmann, “Smooth trajectory planning at the handling limits for oval racing,” Actuators, vol. 11, no. 11, Art. no. 318, Nov. 2022. doi: 10.3390/act11110318
    [11]
    Y. Yan, C. Du, Y. Wang, and D. Pi, “Intelligent vehicle driving decisions and longitudinal-lateral trajectory planning considering road surface state mutation,” Actuators, vol. 14, no. 9, Art. no. 431, Sep. 2025. doi: 10.3390/act14090431
    [12]
    N. Yi, Z. Liu, and X. Yuan, “Highly smooth and parameter independent obstacle avoidance method for autonomous vehicle with velocity-varying obstacle,” PLoS One, vol. 19, no. 6, Art. no. e0303160, Jun. 2024. doi: 10.1371/journal.pone.0303160
    [13]
    W. Yuan, Z. Li, and C.-Y. Su, “Multisensor-based navigation and control of a mobile service robot,” IEEE Trans. Syst., Man, Cybern.: Syst., vol. 51, no. 4, pp. 2624–2634, Apr. 2021. doi: 10.1109/TSMC.2019.2916932
    [14]
    Y. Huang, H. Ding, Y. Zhang, H. Wang, D. Cao, N. Xu, and C. Hu, “A motion planning and tracking framework for autonomous vehicles based on artificial potential field elaborated resistance network approach,” IEEE Trans. Ind. Electron., vol. 67, no. 2, pp. 1376–1386, Feb. 2020. doi: 10.1109/TIE.2019.2898599
    [15]
    T. Guan, Y. Han, M. Kong, S. Wang, D. Feng, and W. Yang, “An improved artificial potential field with RRT star algorithm for autonomous vehicle path planning,” Sci. Rep., vol. 15, no. 1, Art. no. 16982, May 2025. doi: 10.1038/s41598-025-00694-z
    [16]
    W. Li, C. Yang, Y. Jiang, X. Liu, and C.-Y. Su, “Motion planning for omnidirectional wheeled mobile robot by potential field method,” J. Adv. Transp., vol. 2017, Art. no. 4961383, Mar. 2017.
    [17]
    S. S. Ge and Y. J. Cui, “New potential functions for mobile robot path planning,” IEEE Trans. Robot. Automat., vol. 16, no. 5, pp. 615–620, Oct. 2000. doi: 10.1109/70.880813
    [18]
    Z. Jin and S. He, “Anti-rollover artificial potential field motion planning method of an autonomous heavy truck optimised by game theory,” Chin. J. Mech. Eng., vol. 37, no. 1, Art. no. 95, Aug. 2024. doi: 10.1186/s10033-024-01073-x
    [19]
    Y. Liu, K. Bai, H. Wang, and Q. Fan, “Autonomous planning and robust control for wheeled mobile robot with slippage disturbances based on differential flatness,” IET Control Theory Appl., vol. 17, no. 16, pp. 2136–2145, Nov. 2023. doi: 10.1049/cth2.12490
    [20]
    T. B. Burghi, J. G. Iossaqui, and J. F. Camino, “A general update rule for Lyapunov-based adaptive control of mobile robots with wheel slip,” Int. J. Adapt. Control Signal Process., vol. 38, no. 4, pp. 1172–1198, 2024.
    [21]
    J. Bai, M. Ren, J. Li, and Y. Chen, “Trajectory tracking control for wheeled mobile robots subject to longitudinal slippage,” Asian J. Control, vol. 27, no. 5, pp. 2576–2587, 2025.
    [22]
    S. Cong, Z. Wang, M. Li, Y. Jiang, and R. Zhang, “Trajectory tracking of a wheeled mobile robot based on the predefined-time sliding mode control scheme,” Asian J. Control, vol. 26, no. 6, pp. 3275–3290, 2024.
    [23]
    Q. He, Z. Wang, K. Li, Y. Zhang, and M. Li, “Research on autonomous navigation of mobile robots based on IA-DWA algorithm,” Sci. Rep., vol. 15, no. 1, Art. no. 2099, Jan. 2025. doi: 10.1038/s41598-024-84858-3
    [24]
    R. Singh, “A hybrid path planning technique for the time-efficient navigation of unmanned vehicles in an unconstrained environment,” J. Intell. Robot. Syst., vol. 110, no. 3, Art. no. 119, Aug. 2024. doi: 10.1007/s10846-024-02150-y
    [25]
    K. Zhang, Y. Yang, M. Fu, and M. Wang, “Traversability assessment and trajectory planning of unmanned ground vehicles with suspension systems on rough terrain,” Sensors, vol. 19, no. 20, Art. no. 4372, Oct. 2019. doi: 10.3390/s19204372
    [26]
    H. Sira-Ram´ırez and S. K. Agrawal, Differentially Flat Systems. New York, NY, USA: Marcel Dekker, 2004.
    [27]
    C. P. Tang, P. T. Miller, V. N. Krovi, J.-C. Ryu, and S. K. Agrawal, “Differential-flatness-based planning and control of a wheeled mobile manipulator-theory and experiment,” IEEE/ASME Trans. Mechatron., vol. 16, no. 4, pp. 768–773, Aug. 2011. doi: 10.1109/TMECH.2010.2066282
    [28]
    H. Chen, F. Zhang, and B. Aksun-Guvenc, “Collision avoidance in autonomous vehicles using the control Lyapunov function-control barrier function-quadratic programming approach with deep reinforcement learning decision-making,” Electronics, vol. 14, no. 3, Art. no. 557, Jan. 2025. doi: 10.3390/electronics14030557
    [29]
    A. D. Ames, K. Galloway, K. Sreenath, and J. W. Grizzle, “Rapidly exponentially stabilizing control Lyapunov functions and hybrid zero dynamics,” IEEE Trans. Automat. Control, vol. 59, no. 4, pp. 876–891, Apr. 2014. doi: 10.1109/TAC.2014.2299335
    [30]
    Q. Nguyen and K. Sreenath, “Robust safety-critical control for dynamic robotics,” IEEE Trans. Automat. Control, vol. 67, no. 3, pp. 1073–1088, Mar. 2022. doi: 10.1109/TAC.2021.3059156
    [31]
    J. Mattingley and S. Boyd, “CVXGEN: A code generator for embedded convex optimization,” Optim. Eng., vol. 13, no. 1, pp. 1–27, Mar. 2012. doi: 10.1007/s11081-011-9176-9
    [32]
    R. Yang, L. Zheng, J. Pan, and H. Cheng, “Learning-based predictive path following control for nonlinear systems under uncertain disturbances,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 2854–2861, Apr. 2021. doi: 10.1109/LRA.2021.3062805
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (13) PDF downloads(2) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return