In recent years, there has been a growing demand for robotic manipulators to perform tasks in various unstructured environments and situations requiring precision and force control. However, traditional robotic arms have limitations in fully leveraging their advantages in such scenarios. To address this demand, we have designed a cable-driven serpentine manipulator (CDSM) that combines force and precision motion control. This control method allows for precise manipulation of forces and torques at the end-effector, particularly in applications like electric vehicle charging and narrow-space exploration. It also enables independent control in multiple configurations. We achieve force-position hybrid control in task space, ensuring accurate control of end-effector force while achieving precise position control in other directions. Additionally, we implement joint angle closed-loop control in joint space to reduce the impact of cable elasticity deformation and friction on joint motion accuracy. Finally, servo control is applied at the lowest motor level. This paper investigates the modeling, sensing, and control of CDSM within a unified framework of hybrid motion/force control. Through experiments and simulations, we demonstrate the high accuracy and practicality of this control method in various scenarios.

The cable-driven redundant manipulator (CDRM) possesses remarkable flexibility and holds substantial potential for application in constrained environments. To ensure both the smooth movement of the end-effector during delicate operations and the safety of interactions with the surrounding environment, real-time sensing of forces acting on both the end and links is imperative. Current in situ sensor-based methods face limitations in their applicability to CDRMs due to size and load capacity constraints. Moreover, these methods fall short in measuring contact force and its location along the entire arm. In this article, we introduce an ex situ sensing approach for capturing the six-dimensional (6-D) force at the end and the contact force on the linkages of a CDRM. First, a multispace recursive dynamic model of the CDRM is established using the Newton–Euler method. This model establishes mapping relationships among cable tensions, joint torques, and operational forces at the end-effector. Then, a simplified dynamic model for the recursive subsystem is derived based on joint motion transmission relationships and recursive equations. This model decouples the dynamic equations and provides a versatile force-sensing model. It enables the realization of 6-D force/torque sensing at the end-effector, as well as the determination of the magnitude and location of external forces acting on the links. Finally, compliant controllers are designed based on different external force-sensing methods to cater to diverse operational requirements. Experimental validation of the proposed methods is conducted on a CDRM prototype. The results demonstrate that the accuracy of end-effector force sensing exceeds 95%, torque sensing surpasses 90%, and the positioning error of the link's contact force sensing is less than 20 mm. Furthermore, the compliance controllers exhibit excellent smoothness in tasks involving human–robot interaction.

With the rapid development, artificial intelligence (AI) technology may occupy the job positions which require only simple knowledge. Autonomous learning ability has been one of the core abilities that needs to be cultivated in engineering education. However, how to leverage students’ autonomous learning in the practice session of curriculum is still facing several challenges. In view of these challenges, we develop an AI empowered remote experiment platform, which has independent intelligent algorithm center for students to deploy their own algorithms into the real experiment scenarios. Also, in this platform, there is a digital-twins engine to give real-time feedback to the students, helping them to improve their algorithms and even the whole projects. Based on this experiment platform, we propose a teacher-guided autonomous learning practice teaching mode, including autonomous goal setting, autonomous practice process and autonomous feedback optimization, in which the teachers will be a guide to guarantee the learning objective is achieved. A survey was conducted, showing that under this practice teaching mode, students have a deeper understanding of theoretical knowledge, more obvious cultivation of autonomous learning ability, and higher satisfaction with the course.