Explore the vast range of titles available.

Double unmanned surface vehicles (DUSVs) towing a floating rope are more effective at removing large floating garbage on the water’s surface than a single USV. This paper proposes a comprehensive trajectory planner for DUSVs connected with a floating rope for cooperative water-surface garbage collection with dynamic collision avoidance, which takes into account the kinematic constraints and dynamic cooperation constraints of the DUSVs, which reflects the current collection capacity of DUSVs. The optimal travel sequence is determined by solving the TSP problem with an ant colony algorithm. The DUSVs approach the garbage targets based on the guidance of target key points selected by taking into account the dynamic cooperation constraints. An artificial potential field (APF) combined with a leader–follower strategy is adopted so that the each USV passes from different sides of the garbage to ensure garbage capturing. For dynamic obstacle avoidance, an improved APF (IAPF) combined with a leader–follower strategy is proposed, for which a velocity repulsion field is introduced to reduce travel distance. A fuzzy logic algorithm is adopted for adaptive adjustment of the desired velocities of the DUSVs to achieve better cooperation between the DUSVs. The simulation results verify the effectiveness of the algorithm of the proposed planner in that the generated trajectories for the DUSVs successfully realize cooperative garbage collection and dynamic obstacle avoidance while complying with the kinematic constraints and dynamic cooperation constraints of the DUSVs.

In the scenario of water surface garbage detection, the model must accurately detect different types of objects and be able to respond continuously within a short time frame, enabling timely retrieval by the surface cleaning robot. Therefore, this paper proposes a surface garbage detection model named Precise and Real-time YOLO (PAR-YOLO), with a focus on real-time performance and detection accuracy. Firstly, to reduce model computation and improve detection efficiency, the Ghost Bottleneck module is designed and utilized in the backbone section as a replacement for the traditional Bottleneck module. Secondly, in order to effectively reduce the interference of factors such as water ripples, lighting variations, or reflections on object feature recognition, we have designed a Noise Suppression Module (NSM) and integrated it into the neck section. Lastly, to enhance the model’s attention to challenging samples and improve detection accuracy, the Varifocal Loss function is employed in the head section. Experimental results demonstrate that the PAR-YOLO model achieves a Frames Per Second (FPS) of 238, with a mean average precision (mAP) of 85.53%, 47.3%, and 28.5% on our self-made water surface garbage dataset, the Flow public water surface garbage dataset and the Pascal VOC2007 dataset, respectively. Compared to other comparative models, our model achieves the best results.