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Background <p>Slip and dynamic uncertainties significantly degrade the motion control performance of wheeled–legged robots, especially under varying terrain and load redistribution caused by manipulator motion; Objective: to develop an integrated framework for slip-aware modeling, real-time slip estimation, and learning-based control for wheeled–legged robots.</p> Methods <p>A comprehensive kinematic and dynamic model including explicit longitudinal and lateral wheel slip was formulated, a vision-based localization and slip estimation scheme was implemented using an external camera and concentric circular markers, IMU measurements were used for motion characterization and comparison, and a PID + DDPG control strategy was integrated and evaluated through experiments and simulations.</p> Results <p>The proposed framework reduced trajectory tracking error, improved motion stability under slip, produced smoother torque profiles, and showed better pose estimation accuracy with image processing than inertial sensing alone.</p> Conclusion <p>Combining slip-aware modeling, vision-based estimation, and learning-based adaptive control provides a robust and practical solution for wheeled–legged robot motion in slip-prone environments.</p>

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Slip-Aware Modeling and Experimental Evaluation of Wheeled–Legged Robots Using Image Processing and Learning-Based Control

  • 期刊论文

  • 发表日期：

    2026年5月16日

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  • Mostaf jalalnezhad,
  • Sotirios Spanogianopoulos

摘要

Background

Slip and dynamic uncertainties significantly degrade the motion control performance of wheeled–legged robots, especially under varying terrain and load redistribution caused by manipulator motion; Objective: to develop an integrated framework for slip-aware modeling, real-time slip estimation, and learning-based control for wheeled–legged robots.

Methods

A comprehensive kinematic and dynamic model including explicit longitudinal and lateral wheel slip was formulated, a vision-based localization and slip estimation scheme was implemented using an external camera and concentric circular markers, IMU measurements were used for motion characterization and comparison, and a PID + DDPG control strategy was integrated and evaluated through experiments and simulations.

Results

The proposed framework reduced trajectory tracking error, improved motion stability under slip, produced smoother torque profiles, and showed better pose estimation accuracy with image processing than inertial sensing alone.

Conclusion

Combining slip-aware modeling, vision-based estimation, and learning-based adaptive control provides a robust and practical solution for wheeled–legged robot motion in slip-prone environments.