In today’s era of rapid technological advancement, humanoid robots have emerged as a cutting-edge field that integrates mechanical engineering, electronic information technology, computer science, and other disciplines, showcasing high levels of intelligence and realistic human-like characteristics. As a foundational core course in electronic information-related majors, digital electronics provides the underlying logical support for numerous high-tech applications. Integrating humanoid robots into digital electronics education holds significant practical and theoretical importance. On one hand, it brings abstract digital electronics concepts to life, transforming them into tangible applications that students can interact with, thereby enhancing their learning interest and exploration enthusiasm. On the other hand, it fosters the development of versatile talents who not only understand digital electronics principles but can also apply them skillfully in the research, development, and maintenance of humanoid robots, meeting the urgent demands of industrial upgrading for multi-skilled professionals.
Globally, universities and research institutions have started exploring interdisciplinary education integration, with some institutions incorporating humanoid robot projects into electronic engineering courses through practice-oriented teaching models. However, there is still room for improvement in the systematic integration of course knowledge. Domestically, there is growing emphasis on combining cutting-edge technologies with foundational courses, with many universities actively engaging in similar teaching reform practices. Yet, many of these efforts focus on robot competition guidance rather than deeply embedding them into regular course teaching, leaving aspects like teaching method innovation and assessment system adaptation underdeveloped.
The hardware system of humanoid robots, akin to a robust body, includes precision mechanical structures, diverse sensors (e.g., integrated visual, auditory, and tactile systems), and efficient actuators (e.g., motors, servos). Digital electronics technology plays a crucial role in the precise driving, coordinated control, and smooth operation of these hardware components. From a software perspective, the software system of humanoid robots encompasses the robot operating system, motion control algorithms, and intelligent decision-making programs, where underlying operations such as digital signal processing, complex logical operations, and data storage rely on digital electronics. For instance, visual sensors collect vast amounts of image data that must undergo high-speed sampling, quantization, and encoding via digital circuits to convert them into digital signals for subsequent algorithm analysis, enabling the robot to perceive and understand its environment accurately.
In humanoid robots, digital electronics technology is applied in various key areas. For example, microcontroller units (MCUs) generate precise pulse signal sequences based on predefined motion trajectory algorithms to drive motors and servos at desired speeds and angles, enabling smooth limb movements. In perception processing, digital circuits filter and identify valid information from the continuous stream of data from sensor systems, assisting the robot in real-time environmental state judgment. In the decision-making system, intelligent logic modules built on digital logic circuits make decisions by integrating perceptual information. The relationship between humanoid robots and digital electronics can be summarized using logical expressions. For instance, the output of a combinational logic circuit controlling a robot’s hand gesture can be represented as: $$ F = A \cdot B + \overline{C} $$ where A, B, and C are input signals representing different sensor inputs, and F is the output controlling the actuator. Similarly, sequential logic circuits for gait control can be modeled with state equations: $$ Q_{n+1} = D \cdot \overline{Q_n} + \overline{D} \cdot Q_n $$ where D is the input, and Q represents the state of the system.
Current digital electronics courses face several challenges. Firstly, the teaching content often diverges from practical applications, with a strong focus on theoretical knowledge and limited experimental sessions that only cover basic verification-level circuit building. Students struggle to connect classroom knowledge with real-world scenarios, leading to a disconnect between learning and application. Secondly, teaching methods are predominantly single-mode, relying on teacher-led lectures even in blended online-offline models, which keeps students in a passive learning state. While tools like blackboards, PPTs, and videos can clearly present theoretical derivations, they offer few opportunities for autonomous exploration. Given the engaging and hands-on nature of humanoid robots, traditional methods fail to ignite student passion or encourage them to uncover the application potential of digital electronics. Thirdly, assessment methods are inadequate, emphasizing theoretical knowledge through closed-book exams supplemented by attendance, regular assignments, and lab reports. Subjective questions typically revolve around concepts, circuit analysis, small-scale circuit design, and simple calculations, with little inclusion of real-world project case studies or practical操作 assessments. This encourages rote memorization over knowledge application and innovative thinking, leaving graduates ill-prepared for实战 demands.
The goals of this teaching reform are to innovate the digital electronics course model, ensure students deeply master the digital electronics knowledge and skills required for humanoid robot technology, and cultivate their ability to solve complex engineering problems in humanoid robots, such as optimizing motion control circuits and improving perception data processing efficiency. Additionally, we aim to foster innovative thinking and teamwork spirit, equipping students with comprehensive qualities to face future challenges in humanoid robot technology iteration. The reform principles emphasize the close integration of theory and practice, ensuring that course knowledge aligns with humanoid robot project实践, enabling students to learn by doing and understand theory through practice, creating a virtuous cycle.
To optimize teaching content, we introduce humanoid robot cases. For example, at the course outset, we showcase videos of humanoid robot applications, such as dance performances or rescue simulations, to capture student interest and spark curiosity about the underlying digital electronics support. When teaching combinational logic circuits, we use cases like hand gesture control in humanoid robots to illustrate how logic gates design circuits for finger movement commands. In sequential logic circuits, we employ robot gait control examples to explain the role of counters and registers in generating periodic control signals, making abstract circuit knowledge直观 and reducing comprehension difficulty. We also restructure knowledge points around the humanoid robot application主线, following the perception-decision-execution workflow. This integrates topics like sensor interface circuit design, digital signal processing algorithms, and microcontroller programming, helping students build a complete knowledge chain from information acquisition to response. The integration can be summarized in a table:
| Application Workflow Stage | Digital Electronics Topics | Humanoid Robot Example |
|---|---|---|
| Perception | Sensor Interface Circuits, ADC | Visual Data Acquisition |
| Decision | Combinational and Sequential Logic | Gait Control Logic |
| Execution | Microcontroller Programming, Actuator Drivers | Limb Movement Control |
In terms of teaching method innovation, we adopt project-driven learning by designing a series of projects related to humanoid robot functional modules, such as facial expression control systems or autonomous obstacle avoidance platforms. Students are grouped to take charge of a project from需求 analysis to scheme design, circuit building, and debugging. For instance, a project on designing a simple humanoid robot emotion system involves using digital circuits to process input from sensors and control LED displays for expressions, with the logic represented by: $$ E = S_1 \oplus S_2 $$ where S1 and S2 are sensor inputs for different emotions, and E is the output for expression control. We also incorporate virtual simulation teaching using software like Proteus, Multisim, and MATLAB to create virtual environments where students can design and test digital circuits for humanoid robots without hardware constraints. For example, simulating a visual tracking system allows students to adjust circuit parameters and observe output effects, enhancing design skills while reducing costs. Furthermore, we implement group cooperative learning by dividing students into 4-6 person teams based on abilities and characteristics, with roles like hardware搭建, software programming, documentation, and testing. In a music performance project, for instance, students collaborate on circuit design for sound generation and programming for music playback, fostering teamwork and knowledge sharing.
To improve assessment methods, we establish a diversified evaluation system that includes theoretical knowledge (30-40%), practical ability (30-40%), project outcomes (10-20%), and teamwork (10-20%). Theoretical assessments incorporate humanoid robot application scenarios, such as solving problems related to robot control logic using Boolean algebra: $$ Y = A \cdot B + \overline{A} \cdot C $$ where Y is the output for a specific robot action. Practical ability is evaluated through lab operations and project defenses, while project outcomes are scored based on innovation, functionality, and stability. Teamwork is assessed via division of labor合理性 and problem-solving collaboration. We also strengthen process evaluation by tracking classroom performance (10-20%), project progress (30-40%), and lab reports (30-40%), encouraging active participation and detailed documentation of experiments, such as recording steps and solutions for circuit debugging.
In a practical case study, we selected two classes in an electronic information major, with one as the experimental group implementing the reform and the other as the control group using traditional methods. In the experimental group, we introduced humanoid robot科普 lectures, grouped students for project-driven learning with virtual simulation support, held regular progress reports, and organized project exhibitions and defenses, all under diversified assessment. The control group followed textbook chapters with teacher-led lectures and simple experiments, assessed traditionally. After one semester, the experimental group showed an average exam score nearly 10 points higher than the control group, demonstrated stronger innovation in projects (e.g., designing advanced gesture recognition systems), and over 80% reported high course interest compared to about 25% in the control group. However, challenges included limited hardware access leading to queuing and adaptation difficulties for some students with weak foundations. To address this, we pursued industry-academia collaboration to expand equipment and adopted blended online-offline teaching with pre-class materials and post-class tutoring to support all students.
In conclusion, integrating humanoid robots into digital electronics education has led to significant progress in content, methods, and assessment. Teaching content has become case-based and integrated, making knowledge vivid; innovative methods have ignited student passion and practical potential; and improved assessment accurately measures comprehensive quality growth. Students have not only solidified their theoretical foundation in digital electronics but also excelled in humanoid robot applications, laying a solid groundwork for future careers. Looking ahead, as humanoid robot technology advances towards greater intelligence and flexibility, we will further expand interdisciplinary integration by incorporating knowledge from biology and psychology to refine robot design, strengthen international exchanges to learn advanced teaching理念 and technologies, and develop more open and challenging practical projects to cultivate outstanding talents who can lead the humanoid robot era. The continuous evolution of humanoid robots promises to reshape educational paradigms, and we are committed to driving this transformation through sustained reform efforts.
The integration of humanoid robots into digital electronics courses can be mathematically modeled to optimize learning outcomes. For example, the effectiveness of project-based learning on student performance can be represented by a function: $$ L(t) = \alpha \cdot P(t) + \beta \cdot T(t) $$ where L(t) is the learning outcome at time t, P(t) is the project engagement level, T(t) is the theoretical knowledge absorption, and α and β are weights determined by the course design. Similarly, the impact of virtual simulations on skill development can be expressed as: $$ S = \int_{0}^{T} V(t) \cdot C(t) \, dt $$ where S is the skill level, V(t) is the virtual practice intensity, C(t) is the conceptual understanding, and T is the total time. These models help in refining the teaching strategies for humanoid robot applications.
In summary, the reform has demonstrated that humanoid robots serve as a powerful catalyst for enhancing digital electronics education. By aligning course objectives with real-world applications of humanoid robots, we have created a dynamic learning environment that prepares students for the complexities of modern technology. Future work will focus on scaling these initiatives and exploring new frontiers in human-robot interaction, ensuring that education keeps pace with the rapid advancements in humanoid robotics.