Latest Papers

ASME Journal of Mechanisms and Robotics

  • A Small-Scale Integrated Jumping-Crawling Robot: Design, Modeling, and Demonstration
    on June 16, 2025 at 12:00 am

    AbstractThe small jumping-crawling robot improves its obstacle-crossing ability by selecting appropriate locomotion methods. However, current research on jumping-crawling robots remains focused on enhancing specific aspects of performance, and several issues still exist, including nonadjustable gaits, poor stability, nonadjustable jumping posture, and poor motion continuity. This article presents a small jumping-crawling robot with decoupled jumping and crawling mechanisms, offline adjustable gaits, autonomous self-righting, autonomous steering, and certain slope-climbing abilities. The crawling mechanism adopts a partially adjustable Klann six-bar linkage, which can generate four stride lengths and three gaits. The jumping mechanism is designed as a six-bar linkage with passive compliance, and an active clutch allows energy storage and release in any state. The autonomous self-righting mechanism enables the robot to self-right after tipping over, meanwhile providing support, steering, and posture adjustment functions. Prototype experiments show that the designed robot demonstrates good motion stability and can climb a 45 deg slope without tipping over. The robot shows excellent steering performance, with a single action taking 5 s and achieving a steering angle of 11.5 deg. It also exhibits good motion continuity, with an average recovery time of 12 s to return to crawling mode after a jump. Crawling experiments on rough terrain demonstrate the feasibility of applying the designed robot in real-world scenarios.

A Hybrid Method Combining Data-Driven and Model-Based Algorithms for External Force-Sensing and Haptics Control of Cable-Pulley-Driven Surgical Robotic Manipulator

Abstract

The implementation of the high-precision tracking control and external force-sensing ability of a manipulator is important for achieving refined surgical robot operation. In this paper, a hybrid method based on data-driven and model-based algorithms is proposed for the manipulator of a cable-pulley-driven surgical robot. This method integrates an artificial neural network and a dynamic model rotation angle estimation, and a full closed-loop control architecture is further constructed. The algorithm compensates for the hysteresis of the joint angle and effectively improves the tracking control precision. Based on the architecture, the external force estimator (EFE) using a joint torque disturbance observer and the force interaction teleoperation control strategy using a direct force feedback framework (DFF) are implemented. In the force loading experiment, it was shown that the EFE performs well for static and dynamic force estimation, and the teleoperated haptic control experiment showed that the DFF-EFE-based system has a high position-tracking accuracy with real-time external force-sensing ability.

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