The posture inversion of magnetic liquid robots is a fundamental area of research for precise manipulation. Conducting relevant experiments enables students to master essential technologies, including magnetic field sensor array design and posture inversion methods, while deepening their understanding of concepts such as medical-engineering integration and electromagnetic control. This also fosters students’ engineering practice and scientific research innovation skills, laying a solid foundation for developing interdisciplinary talents.

This study presents an experimental teaching platform for the posture inversion of magnetic liquid robots. A magnetic liquid robot designed for the human aortic environment is fabricated using silicone 3D printing. The posture inversion challenge is reformulated as a minimization problem involving an objective error function, for which we propose an improved differential evolution algorithm with an adaptive operator. In addition, to achieve an economical and lightweight design, the magnetic field sensor array is optimized based on each sensor’s contribution value and spatial uniformity constraints. Based on this experimental teaching platform, a three-level experimental scheme consisting of basic, advanced, and innovative experiments is designed. The basic experiment, “Data Acquisition and Processing of Magnetic Liquid Robot Posture,” enables students to learn about medical magnetic liquid robot posture inversion, magnetic field sensor array measurement methods, and position information processing. The advanced experiment, “Posture Inversion Experiments of Magnetic Liquid Robot under Different Bending Deflections,” teaches students about the improved differential evolution algorithm model based on neural networks, verifies the fundamental response relationships between position information and posture, and facilitates simple posture inversion. The innovative experiment, “Posture Inversion of Magnetic Liquid Robot in a Simulated Blood Vessel Environment,” establishes a static aortic model, optimizes the posture inversion algorithm, and enhances inversion accuracy in complex structures. This platform integrates theoretical knowledge with practical applications to assess student understanding of magnetic liquid robots, magnetic sensor arrays, and posture inversion techniques.

In posture inversion experiments with deflection angles ranging from 0° to 40°, the actual posture of the magnetic liquid robot closely aligns with the desired inverted posture, showing a maximum error of 3.2 mm and an average prediction error of less than 2 mm. In experiments conducted in a simulated blood vessel environment, the maximum error of measurement points is 3.5 mm, with an average prediction error of less than 2 mm. These results confirm the model’s effectiveness in three-dimensional spatial posture inversion and the realization of the posture inversion of a magnetic liquid robot.

This platform significantly supports the development of practical skills and innovative thinking among students majoring in electrical engineering, biomedical engineering, and robotics within the context of emerging engineering education. It aligns with the principles of interdisciplinary and innovative education, providing a high-quality environment for student practice and innovation. The platform is expected to have a broad impact across various scenarios and contribute to cultivating high-quality, practice-oriented talents in magnetic liquid robot control and intelligent algorithm utilization.