  In the crucial stages of the Robomaster Youth Championship, the Robomaster EP
Robot must operate exclusively on autonomous algorithms to remain competitive.
Target recognition and automatic assisted aiming are indispensable for the EP
robot. In this study, we use YOLOv5 for multi-object detection to identify the
Robomaster EP Robot and its armor. Additionally, we integrate the DeepSORT
algorithm for vehicle identification and tracking. As a result, we introduce a
refined YOLOv5-based system that allows the robot to recognize and aim at
multiple targets simultaneously. To ensure precise tracking, we use a PID
controller with Feedforward Enhancement and an FIR controller paired with a
Kalman filter. This setup enables quick gimbal movement towards the target and
predicts its next position, optimizing potential damage during motion. Our
proposed system enhances the robot's accuracy in targeting armor, improving its
competitive performance.
