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.

翻译：在Robomaster青少年挑战赛的关键阶段，Robomaster EP机器人必须完全依赖自主算法运行以保持竞争力。目标识别与自动辅助瞄准对于EP机器人而言不可或缺。本研究采用YOLOv5进行多目标检测，以识别Robomaster EP机器人及其装甲模块。同时，我们集成DeepSORT算法实现车辆识别与跟踪。由此，我们提出一种基于改进YOLOv5的系统，使机器人能够同时识别并瞄准多个目标。为确保精确跟踪，我们采用带前馈增强的PID控制器以及结合卡尔曼滤波器的FIR控制器。该配置能令云台快速转向目标并预测其下一时刻位置，从而在运动过程中优化潜在打击效果。我们提出的系统提升了机器人瞄准装甲的精确度，进而增强了其竞赛表现。