Due to the wavelength-dependent light absorption and scattering, the raw underwater images are usually inevitably degraded. Underwater image enhancement (UIE) is of great importance for underwater observation and operation. Data-driven methods, such as deep learning-based UIE approaches, tend to be more applicable to real underwater scenarios. However, the training of deep models is limited by the extreme scarcity of underwater images with enhancement references, resulting in their poor performance in dynamic and diverse underwater scenes. As an alternative, enhancement reference achieved by volunteer voting alleviate the sample shortage to some extent. Since such artificially acquired references are not veritable ground truth, they are far from complete and accurate to provide correct and rich supervision for the enhancement model training. Beyond training with single reference, we propose the first comparative learning framework for UIE problem, namely CLUIE-Net, to learn from multiple candidates of enhancement reference. This new strategy also supports semi-supervised learning mode. Besides, we propose a regional quality-superiority discriminative network (RQSD-Net) as an embedded quality discriminator for the CLUIE-Net. Comprehensive experiments demonstrate the effectiveness of RQSD-Net and the comparative learning strategy for UIE problem. The code, models and new dataset RQSD-UI are available at: https://justwj.github.io/CLUIE-Net.html/.

How to use environmental information to improve the cooperative chasing efficiency of multiple unmanned surface vehicles (multi-USV) is a problem worthy of attention. This article considers an archipelago scenario and proposes an obstacle-assisted chasing framework based on reinforcement learning, in which a multi-USV system chases a smart evader through autonomous environmental awareness and target state prediction. When there are obstacles near the evader, the chasing group can use them to quickly complete the chase. Otherwise, within the range of perception, the group can drive the evader closer to the obstacles or directly cooperate to catch the evader. At the same time, the problem of group credit allocation is also considered, so as to achieve a relatively stable encircling structure with the least number of vehicles. Simulation experiments in the multi-obstacle environment demonstrate the flexibility of the proposed framework. Compared with the traditional force-based and learning-based methods in untrained archipelago environments, the model trained by this framework is proven to have higher efficiency while ensuring real-time performance and generalization. Note to Practitioners —The motivation of this paper is to provide guidance to the USV team in learning how to collaboratively chase and capture target through multi-agent reinforcement learning. Meanwhile, this method can also be applied to other chasing scenarios for vehicles, such as ground robots. This paper proposes a new method that leverages the MA-POCA learning framework and RSA mechanism to enhance cooperation and credit allocation among USVs. Additionally, a novel reward mechanism is designed to encourage USVs to fully utilize the advantages of the surrounding environment and accelerate cooperative chasing. With this method, multiple USVs can effectively pursue intelligent evaders through autonomous environmental perception and target state prediction. To validate the proposed method, we developed a high-fidelity simulation environment using Unity3D, incorporating USV dynamics, irregular obstacles, and sea surface disturbances. Furthermore, our method demonstrates low computational cost, making it suitable for practical USV navigation and control applications in the future.

This article proposes an underwater climbing adsorption robot. The robot avoids coming into contact with adsorbates by using a mechanism of negative pressure adsorption based on Bernoulli's theorem, which allows it to generate a maneuverable force. A synchro-drive locomotion mechanism is developed to boost the maneuverability of the robot such that it can carry out omnidirectional motion using the least mechanical parts. Discrete and continuous modes of locomotion are proposed and verified to enable the robot to follow a complex path, and a switch strategy to balance its adsorption and locomotion is developed. In stationary operation mode, the adsorption force can be increased by reducing the gap to maintain the stability of the robot when carrying loads. In the motion mode, the size of the gap is increased to lend the robot a powerful obstacle-surmounting capacity when it moves on rough and curved surfaces. The results show that the proposed robot has the advantages of being non-contact, and having high adsorption and mobility that are suitable for challenging operations on a variety of underwater structural surfaces.

Underwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.