The Future Robotics program at the School of Future Technology has established a forward-looking, interdisciplinary curriculum system. Breaking away from traditional single-discipline boundaries, the curriculum deeply integrates Robotics, Artificial Intelligence, Electronic Control, Advanced Manufacturing, and Systems Science. It aims to cultivate future leaders with systemic thinking, innovation capabilities, and a global perspective.

【Fall Semester Courses】

Unified Robotics II

Course Code: B6210110 | Credits: 6

Course Description: This is a core course in robotics control and actuation. It systematically covers robot kinematics, dynamics, and control methods. Topics include forward/inverse kinematics modeling, Jacobian matrices and singularity analysis, Lagrangian dynamics, principles of motor drive and servo control, robot trajectory planning, and position/force/impedance control strategies. The course emphasizes deep integration of theory and experimentation. Through hands-on practice on robotic control platforms, students develop skills in motion control algorithm design and electric drive system debugging, forming a foundational pillar for intelligent robotic motion control.

Optimization Methods

Course Code: B6210490 | Credits: 3

Course Description: This course systematically introduces mathematical optimization theory and its practical applications in robotic engineering. Topics include linear programming, nonlinear programming, convex optimization, and numerical optimization algorithms. It provides students with essential mathematical tools for robot trajectory optimization, parameter identification, and machine learning algorithm design.

Reinforcement Learning

Course Code: B6210220 | Credits: 2

Course Description: As a cutting-edge core course in artificial intelligence, this course explores theories and methods through which intelligent agents learn optimal decision-making strategies via trial-and-error interaction in uncertain environments. Topics include Markov Decision Processes, value function methods, policy gradient algorithms, and deep reinforcement learning. It serves as a key technological foundation for autonomous decision-making and adaptive control in robotics.

Advanced Design of Micro-Nano Systems

Course Code: B0203332 | Credits: 2

Course Description: Focusing on Micro-Electro-Mechanical Systems (MEMS) and microrobotics, this course systematically covers physical effects at micro-nano scales, micro-nano fabrication processes, design of micro-sensors/actuators, and system integration methods. It cultivates students’ innovative design capabilities for micro-nano robotic systems.

Data Structures and Algorithms

Course Code: B6210440 | Credits: 2

Course Description: This is a foundational theoretical course for computer science and robotic software development. It systematically covers core data structures (linear lists, trees, graphs, etc.) and classic algorithm design paradigms (sorting, searching, dynamic programming, etc.), cultivating students’ algorithmic thinking and ability to efficiently solve complex computational problems.

Signals and Systems

Course Code: B6210080 | Credits: 3

Course Description: A core theoretical foundation in automation and information processing. This course systematically analyzes the time-domain, frequency-domain, and time-frequency characteristics of continuous and discrete signals. It provides in-depth explanation of linear time-invariant system response analysis and system functions, laying the theoretical groundwork for robotic sensor signal processing and control system design.

Robot Materials and Forming

Course Code: B6210050 | Credits: 3

Course Description: This course explores novel lightweight high-strength materials, smart materials, and functional composites for robotic applications. It systematically covers advanced manufacturing processes such as additive manufacturing (3D printing) and precision forming. Emphasizing an integrated material-structure-function design approach, it cultivates students’ complete engineering capabilities from material selection to structural realization.

Electronic Circuits and Systems II

Course Code: B6210030 | Credits: 5

Course Description: This is an advanced core course in robotics hardware, systematically covering digital electronics and embedded system fundamentals. Topics include digital logic gates, combinational and sequential logic design, memory and programmable logic devices, microcontroller architecture, and embedded system development basics. The course emphasizes deep integration of theory and practice. Through lab projects, students develop capabilities in digital system design, embedded programming, and hardware-software co-design, serving as a core foundation for robotic control system hardware implementation.

Probability Theory and Mathematical Statistics

Course Code: B07M3010 | Credits: 3

Course Description: A mathematical foundation course for robotic perception and decision-making. This course systematically covers random variables, probability distributions, parameter estimation, and hypothesis testing, providing the probabilistic and statistical foundation for robot state estimation, sensor fusion, and machine learning algorithms.

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【Spring Semester Courses】

Unified Robotics I

Course Code: B6210100 | Credits: 6

Course Description: This is a core course in robotics mechanical fundamentals, systematically covering the design and analysis methods of robotic mechanical systems. Topics include robot mechanism types and configuration design, kinematic principles of linkage mechanisms, mechanical transmission systems, joint and actuator design, structural stiffness and strength analysis, and mechanical system optimization design. Through a combination of theoretical instruction and mechanical design practice, the course cultivates students' abilities in robotic mechanical body design and engineering drawing standards, laying a solid mechanical foundation for robotic system development.

Robot Dynamics

Course Code: B6210090 | Credits: 4

Course Description: This course specializes in force analysis and dynamic characteristic modeling of robotic mechanisms. It systematically covers dynamic modeling methods such as Lagrangian formulation and Newton-Euler formulation, providing in-depth analysis of how inertial parameters, Coriolis forces, gravity, and friction affect robotic dynamic performance. The course provides accurate theoretical model support for the design of high-performance, high-dynamic robot controllers.

Principles of Automatic Control

Course Code: B6210070 | Credits: 3

Course Description: A core course in classical control theory, systematically covering the modeling, analysis, and synthesis methods for linear systems. Topics include time-domain analysis, root locus methods, frequency-domain analysis, and system compensation design. Students master methods for analyzing and optimizing control system stability, accuracy, and responsiveness, forming the theoretical foundation for robotic motion control.

Introduction to Robot Intelligence

Course Code: B6210060 | Credits: 4

Course Description: A foundational core course in robotic perception and cognition. It systematically introduces fundamentals of computer vision, multi-sensor information fusion, SLAM technology, and intelligent decision-making methods. Students learn how robots acquire environmental information, construct environmental maps, and make autonomous decisions, endowing robots with perceptual and cognitive capabilities.

Introduction to Information Communication Networks

Course Code: B6210130 | Credits: 2.5

Course Description: A foundational course in communication technologies for networked robotic systems. It covers computer network architectures, communication protocols, Internet of Things technologies, and distributed system fundamentals. Students understand data interaction mechanisms and system architecture design methods in scenarios such as multi-robot coordination, cloud robotics, and teleoperation.

Electronic Circuits and Systems I

Course Code: B6210020 | Credits: 5

Course Description: This is a core foundational course in robotics hardware, systematically covering circuit theory fundamentals and analog electronics. Topics include basic circuit laws and analysis methods, steady-state and transient analysis of DC/AC circuits, semiconductor device characteristics, basic amplifier circuit design, and frequency response analysis. Through a combination of theoretical instruction and experimental training, the course cultivates students' solid circuit analysis abilities and analog system hardware design thinking, laying a strong foundation for subsequent electronic system courses.

Computational Thinking and Programming Practice

Course Code: B61G0602 | Credits: 2.5

Course Description: This is a core foundational course in programming languages for robotic software development. It systematically covers the core syntax features of the C++ programming language and the object-oriented programming paradigm. Topics include data types and memory management, flow control structures, functions and recursion, classes and objects, inheritance and polymorphism mechanisms, as well as fundamental data structures and algorithm implementation. Through project-driven programming practice, the course strengthens students' coding standards, debugging abilities, and algorithmic thinking, laying a solid language foundation for subsequent development in robotic operating systems, perception and decision-making algorithm implementation, and large-scale software system construction.

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【Summer School Practice】

Embedded Systems and Course Design

Course Code: B6210450 | Credits: 3

Course Description: This is a comprehensive practice course based on the embedded Linux operating system. Students are required to write programs in the Linux environment to control an intelligent car platform, completing tasks such as visual recognition (e.g., icon identification), automatic line tracking, and autonomous decision-making actions. The course covers embedded application development, fundamentals of machine vision, and behavioral decision logic, cultivating students' full-link development capabilities in robot perception-decision-control on the Linux platform, and strengthening system integration abilities in complex scenarios.

Robot Innovation Practice I

Course Code: B6210320 | Credits: 1

Course Description: This is a specialized practice module focused on mechanical design and modeling. Based on mechanical fundamentals, students are required to use 3D CAD software to complete robotic mechanical structure design and digital modeling. Typical tasks include the structural design and assembly simulation of robotic arms. The course emphasizes the transformation from theoretical design to digital models, strengthening engineering drawing standards and mechanical structure validation literacy, providing accurate digital models for physical manufacturing.

Robot Innovation Practice II

Course Code: B6210330 | Credits: 1

Course Description: This is a specialized practice module focused on mechatronic system integration and control. Based on the theory from Unified Robotics II, students are required to complete the hardware installation of motor drive systems and the deployment of motion control algorithms to achieve precise motion execution of robotic arms. The course emphasizes software-hardware co-debugging capabilities, transforming kinematic solutions into motor drive commands to achieve actual motion control of the robot body, completing the transition from static structures to dynamic systems.

Industrial Systems Cognition

Course Code: B81M0070 | Credits: 0.5

Course Description: Through enterprise visits, expert lectures, and case study discussions, students gain in-depth understanding of modern industrial manufacturing processes, automated production lines, and industrial robot application scenarios. The course builds engineering intuition and industry perspective, clarifying professional development directions.

Basic Practice of Mechanical Manufacturing

Course Code: B81M0010 | Credits: 1

Course Description: A foundational practice course combining traditional metalworking practice with modern manufacturing technologies. Through hands-on training in turning, milling, fitting, welding, and CNC machining, students master basic mechanical processing techniques and operational skills, establishing intuitive understanding of manufacturing processes and hands-on capabilities.