Meng Cheng Lau

This paper presents the development and evaluation of an autonomous archery system for humanoid robots. The purpose of the project was to evaluate how effectively a humanoid robot can perform a precision sports task that requires coordinated biomechanics, reliable perception, and consistent autonomous decision-making. Archery was selected because it integrates key components of humanoid robotics, such as kinematics, balance, vision, and timing, within a single repeatable activity. The robot operated in ROS1 using Dynamixel actuators and a custom bow and arrow setup. A YOLOv9 vision system was implemented to detect the target in real time and provide accurate positions. The logic combined perception, planned alignment, predefined joint motion sequences, and time-based synchronisation for arrow release. All experiments were carried out physically in a controlled indoor environment. The robot, Polaris, was tested multiple times at varying target rotation speeds, and performance was evaluated through accuracy, repeatability, latency, and overall stability of the system. The results showed that Polaris could consistently hit the target at moderate rotation speeds, with performance decreasing as the target speed increased. The system demonstrated reliable integration of perception and motion control, with successful hits achieved. Key limitations included occasional actuator torque issues, vision latency after extended operation, and sensitivity to battery voltage during movement. Despite these constraints, the project achieved its primary objective of demonstrating that a humanoid robot can autonomously perform archery with measurable accuracy using lightweight control methods and realtime visual feedback.

Brooke Zita Vrbanic, Meng Cheng Lau Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026

Autonomous driving remains a challenging problem in mobile robotics due to the need for reliable perception, decision making, and control under limited computational resources. This paper presents a lightweight hybrid vision framework for autonomous driving in the FIRA Urban Challenge using the AGILEX LIMO platform. The proposed system combines classical computer vision techniques for lane and obstacle detection with a YOLOv8n-based traffic sign recognition module. A hierarchical finite state machine architecture coordinates line following, junction handling, traffic sign interpretation, and obstacle avoidance, while a proportional-derivative controller generates real-time steering commands. To evaluate the suitability of learning-based perception for embedded robotic platforms, a comparative study between AprilTag and YOLO-based traffic sign recognition was conducted. Experimental results demonstrate that although AprilTags provide superior geometric accuracy, the YOLO-based approach offers greater robustness under partial occlusion, varying illumination conditions, and non-ideal viewing angles. Operating entirely on onboard CPU resources using only the RGB stream from an RGB-D camera, the proposed framework achieved an average processing rate of 14.5 FPS while successfully performing autonomous navigation tasks in FIRA-style environments. The results demonstrate that combining lightweight deep learning with classical vision methods provides an effective solution for embedded autonomous driving applications.

Walter Iran Amador Valenzuela, Bo Zhou, Meng Cheng Lau Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026