The award results of the TI Cup National Undergraduate Electronic Design Contest 2025 have recently been announced. In the undergraduate category, 89 entries were recommended for national awards, including 27 provincial first prizes, 131 second prizes, and 223 third prizes. A team from ZJUI—Chi Guanzhang, class of 2027 in Electrical Engineering, Li Xiang, class of 2028 in Computer Engineering, and Gu Mingzhi, class of 2026 in Electrical Engineering—won the national second prize.
The team designed an autonomous tracked vehicle with a laser aiming module based on the TI MSPM0 microcontroller. By focusing on embedded control, sensor fusion, motion control, and real-time algorithms, they enabled precise static and dynamic aiming, continuous shooting, and even laser-drawn light patterns.
Facing issues like vision recognition instability and insufficient motor power, they applied knowledge from programming, embedded systems, and analog signal processing courses to optimize algorithms, motor selection, and mechanical design, significantly improving system robustness and stability.
During the contest, the team focused on key technologies such as embedded control, sensor fusion, motion control, and real-time algorithms, significantly improving the car’s stability and shooting accuracy. With the innovative design of "intelligent hardware + real-time control," the car could not only achieve precise static aiming but also realize continuous shooting while moving. It even drew specific light track patterns using a laser pointer, fully demonstrating the system’s intelligence and practical application potential.
They shared that the process was not smooth. "During the car’s test operation, we found that the OpenMV library, which the visual module relies on, saw a significant drop in recognition rate under complex lighting conditions and perspective changes, with obvious jitter in positioning results," they said.
Benefiting from the knowledge of programming, data structures, and memory management acquired in ZJUI’s Computer Systems and Programming (ECE 220) course, as well as interdisciplinary research training, the team members quickly collaborated to accurately identify key issues. They applied the knowledge of pointer operations and dynamic memory allocation learned in the course to implement more complex algorithms with limited hardware resources, and carried out engineering optimizations in aspects such as ROI (Region of Interest) setting, candidate contour screening, and digital filtering, thereby significantly enhancing the system’s robustness.
In terms of hardware, the team also encountered problems of insufficient overall vehicle power and excessive height after vehicle assembly. "At that time, we used the knowledge of circuit analysis and system modeling from the Analog Signal Processing (ECE 210) course to find a breakthrough," the members recalled. By analyzing the characteristics of the motor drive circuit, they temporarily replaced the car’s motor with a higher-power one and independently designed and processed connectors to adjust the vehicle height to the compliant range. This not only met the competition standards but also improved the stability of the mechanical structure.
Reflecting on the experience, the team highlighted that the competition allowed them to integrate knowledge across multiple disciplines—from electronics and programming to system integration—turning theory into practice and building confidence in solving complex engineering problems.
▲ Debugging of 2D Gimbal for Inertial Navigation Vision
▲Tracking Operation of the Car
