A Low-Latency Mobile Robot Target Following Method Based on Improved YOLO-World

This paper addresses the challenges of high latency and the lack of an effective recovery strategy in mobile robot target following tasks. In this paper, a low-latency mobile robot target tracking method based on the improved YOLO-World algorithm is proposed. The process primarily consists of three parts: target detection, target tracking, and motion control. First, for target detection, we introduce a tailored lightweight backbone network, GSS, within the YOLO-World framework, which progressively expands the receptive field through cascaded convolutional operations and enhances cross-group feature interaction via a channel mixing mechanism, significantly improving model efficiency with minimal loss in detection accuracy. Additionally, depthwise separable convolution is applied to the detection head to reduce computational redundancy. Secondly, in the target tracking part, a lightweight target tracking algorithm based on improved BoT-SORT is adopted, and the tracking delay is effectively reduced by optimizing the ReID feature extraction backbone network. Then, the motion control part adopts an active search strategy based on visual servo control. When the tracked target is lost, the strategy utilizes a camera motion compensation-based tracker to predict the target motion state and controls the robot to actively search for the target accordingly. Subsequently, feature tracking is resumed through target re-recognition, thus re-establishing target following. Experiments on public datasets and real-world scenarios demonstrate that the proposed method achieves strong robustness and real-time performance.