This article proposes a deformable water-mobile robot that can be used for rescue work. The robot body adopts an open-motion chain structure with two degrees of freedom, including two drive modules and one main control module. The three modules are connected through deformation joints, and each drive module is equipped with an underwater thruster. The robot can obtain a triangle, linear shape, curved shape, and U-shape through deformation and have three types of motion: linear shape motion, U-shaped motion, and curved shape motion. In the linear shape, a multi-island genetic algorithm was used to optimize the structural parameters with the minimum resistance and the maximum volume. Floating state analysis was conducted in the U-shape, and the structural parameters were reasonably designed. By experimenting with the robot prototype on water, the robot can achieve oscillating, linear, U-shaped, and horizontal rotary motion, has an automatic adjustment function, and effective buoyancy meets the required requirements.

In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images-the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.