Overview
The KN2C Small Size League (SSL) project features a group of mobile robots designed for robotic soccer competition. Integrating omnidirectional drive systems, low-latency vision processing, and multi-agent strategy, the system operates in a dynamic environment where robots must execute precise ball handling and tactical plays at high speeds.
Functional Summary
KN2C SSL is an end-to-end robotics framework developed for the RoboCup Small Size League. My journey with the team evolved from Mechanical Design and Prototyping to lead roles in Control Systems and Computer Vision. The project centers on the coordination of six autonomous robots performing in a fast-paced soccer match, integrating global vision data to perform real-time path planning and high-frequency motor control.
Mechanical Prototyping
In the early stages, I was responsible for the CAD design, fabrication, and maintenance of the 4-wheel omnidirectional drive systems and solenoid-based kicking mechanisms.
Control & Optimization
Later, I transitioned to developing trajectory optimization algorithms and motion control loops to ensure smooth, high-speed movement under strict physical constraints.
Competitive Excellence
Under my leadership, the team secured 2nd place in the Machine Vision Competition (2014). Building on that foundation, we refined our vision pipeline to ultimately reach 1st place in 2015.
Project Milestones
Hardware Design
Designed and maintained the mechanical structure, optimizing weight distribution and solenoid efficiency for powerful kicks.
Algorithm Dev
Implemented Kalman filters for ball tracking and real-time trajectory planners for dynamic obstacle avoidance.
Leadership
Led a multidisciplinary team through rigorous testing phases to win national technical recognition.
From Mechanics to Machine Vision
Full-Cycle Prototyping
Taking the robots from the drawing board to the field involved hands-on assembly, electronics troubleshooting, and continuous mechanical maintenance to withstand the intensity of competitive play.
Vision-Guided Autonomy
My final year was dedicated to the machine vision pipeline, where we optimized low-latency object detection and pose estimation, leading to our second place in 2014 and first place in 2015.
Technical Stack & Skills