The International Maritime Organization (IMO) proposed the concept of a maritime autonomous surface ship (MASS), which could integrate perception, decision-making, and control functions to implement autonomous navigation in complicated environments. It is recognized as the fundamental component of the future new generation of shipping systems. The development of MASS requires the use of specific test tools and use cases for the evaluation of the safety, reliability, and functional performance of its navigation system. At present, the lack of standardized, modular, and serial testing paradigms and methods for MASS impedes the development and improvement of related products and applications. Therefore, it is essential to develop a practical and applicable testing framework. This paper presents a functional analysis of autonomous navigation systems and provides a comprehensive overview of current research status, potential solutions, and future challenges. Moreover, this paper also takes a step towards facilitating the functional testing of autonomous surface vehicles by proposing a hybrid virtual-real framework consisting of scenario generation, virtual simulation, model-scaled physical experiment, validation and evaluation. The research opportunities and future aspects are also addressed. This work may serve as a reference for academic and industrial researchers to investigate new methods and to develop prototype systems for future autonomous surface ships.

This paper introduces TexGarment, an efficient method for synthesizing high-quality, 3D-consistent garment textures in UV space. Traditional approaches based on 2D-to-3D mapping often suffer from 3D inconsistency, while methods learning from limited 3D data lack sufficient texture diversity. These limitations are particularly problematic in garment texture generation, where high demands exist for both detail and variety. To address these challenges, TexGarment leverages a pre-trained text-to-image diffusion Transformer model with robust generalization capabilities, introducing structural information to guide the model in generating 3D-consistent garment textures in a single inference step. Specifically, We utilize the 2D UV position map to guide the layout during the UV texture generation process, ensuring a coherent texture arrangement and enhancing it by integrating global 3D structural information from the mesh surface point cloud. This combined guidance effectively aligns 3D structural integrity with 2D layout. Our method efficiently generates high-quality, diverse UV textures in a single inference step while maintaining 3D consistency. Experimental results validate the effectiveness of TexGarment, achieving state-of-the-art performance in 3D garment texture generation.

Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps. Developing an effective and efficient algorithm that is capable of automatically generating high-quality PBR materials rather than RGB texture for 3D meshes can significantly streamline the 3D content creation. Most existing methods leverage pre-trained 2D diffusion models for multi-view image synthesis, which often leads to severe inconsistency between the generated textures and input 3D meshes. This paper presents TexGaussian, a novel method that uses octant-aligned 3D Gaussian Splatting for rapid PBR material generation. Specifically, we place each 3D Gaussian on the finest leaf node of the octree built from the input 3D mesh to render the multi-view images not only for the albedo map but also for roughness and metallic. Moreover, our model is trained in a regression manner instead of diffusion denoising, capable of generating the PBR material for a 3D mesh in a single feed-forward process. Extensive experiments on publicly available benchmarks demonstrate that our method synthesizes more visually pleasing PBR materials and runs faster than previous methods in both unconditional and text-conditional scenarios, exhibiting better consistency with the given geometry. Our code and trained models are available at https://3d-aigc.github.io/TexGaussian.

This paper focuses on synthesizing high-quality and complete textures directly on the surface of 3D models within 3D space. 2D diffusion-based methods face challenges in generating 2D texture maps due to the infinite possibilities of UV mapping for a given 3D mesh. Utilizing point clouds helps circumvent variations arising from diverse mesh topologies and UV mappings. Nevertheless, achieving dense point clouds to accurately represent texture details poses a challenge due to limited computational resources. To address these challenges, we propose an efficient octree-based diffusion pipeline called TexOct. Our method starts by sampling a point cloud from the surface of a given 3D model, with each point containing texture noise values. We utilize an octree structure to efficiently represent this point cloud. Additionally, we introduce an innovative octree-based diffusion model that leverages the denoising capabilities of the Denoising Diffusion Probabilistic Model (DDPM). This model gradually reduces the texture noise on the octree nodes, resulting in the restoration of fine texture. Experimental results on ShapeNet demonstrate that TexOct effectively generates high-quality 3D textures in both unconditional and text / image-conditional scenarios.

Soft robots have shown remarkable distinct capabilities due to their high deformation. Recently increasing attention has been dedicated to developing fully soft robots to exploit their full potential, with a recognition that electronic powering may limit this achievement. Alternative powering sources compatible with soft robots have been identified such as combustion and chemical reactions. A further milestone to such systems would be to increase the controllability and responsiveness of their underlying reactions in order to achieve more complex behaviors for soft robots. In this paper, we present a thermally-activated reactor incorporating a biocompatible hydrogel valve that enables control of the biochemical reaction of sugar and yeast. The biochemical reaction is utilized to generate contained pressure, which in turn powers a fluidic soft actuator. Experiments were conducted to evaluate the response time of the hydrogel valves with three different crosslinker concentrations. Among the tested concentrations, we found that the lowest crosslinker concentration yielded the fastest response time of the valve at an ambient temperature of 50°C. We also evaluated the pressure generation capacity of the reactor, which can reach up to 0.22 bar, and demonstrated the thermoresponsive behavior of the reactor to trigger a biochemical reaction for powering a fluidic soft actuator. This work opens up the possibility to power and control tetherless and fully soft robots.

This paper considers deep visual recognition on long-tailed data. To make our method general, we tackle two applied scenarios, i.e. , deep classification and deep metric learning. Under the long-tailed data distribution, the most classes (i.e., tail classes) only occupy relatively few samples and are prone to lack of within-class diversity. A radical solution is to augment the tail classes with higher diversity. To this end, we introduce a simple and reliable method named Memory-based Jitter (MBJ). We observe that during training, the deep model constantly changes its parameters after every iteration, yielding the phenomenon of weight jitters. Consequentially, given a same image as the input, two historical editions of the model generate two different features in the deeply-embedded space, resulting in feature jitters. Using a memory bank, we collect these (model or feature) jitters across multiple training iterations and get the so-called Memory-based Jitter. The accumulated jitters enhance the within-class diversity for the tail classes and consequentially improves long-tailed visual recognition. With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, i.e., deep image classification and deep metric learning (on long-tailed data). Extensive experiments on five long-tailed classification benchmarks and two deep metric learning benchmarks demonstrate significant improvement. Moreover, the achieved performance are on par with the state of the art on both tasks.

This paper tackles the cross-modality person re-identification (re-ID) problem by suppressing the modality discrepancy. In cross-modality re-ID, the query and gallery images are in different modalities. Given a training identity, the popular deep classification baseline shares the same proxy (i.e., a weight vector in the last classification layer) for two modalities. We find that it has considerable tolerance for the modality gap, because the shared proxy acts as an intermediate relay between two modalities. In response, we propose a Memory-Augmented Unidirectional Metric (MAUM) learning method consisting of two novel designs, i.e., unidirectional metrics, and memory-based augmentation. Specifically, MAUM first learns modality-specific proxies (MS-Proxies) independently under each modality. Afterward, MAUM uses the already-learned MS-Proxies as the static references for pulling close the features in the counterpart modality. These two unidirectional metrics (IR image to RGB proxy and RGB image to IR proxy) jointly alleviate the relay effect and benefit cross-modality association. The cross-modality association is further enhanced by storing the MS-Proxies into memory banks to increase the reference diversity. Importantly, we show that MAUM improves cross-modality re-ID under the modality-balanced setting and gains extra robustness against the modality-imbalance problem. Extensive experiments on SYSU-MMOI and RegDB datasets demonstrate the superiority of MAUM over the state-of-the-art. The code will be available.

With the advent of Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) technology provide enormous opportunities and support for intelligent manufacturing. Material handling is vital in manufacturing systems to ensure that proper materials with the right quantity and quality can be delivered to each machine or workstation at the right time. AGV has been widely used in smart factories for material handling, and AGV scheduling plays a critical role in practical AGV application. However, the AGV scheduling problem becomes more and more complex with the development of intelligent manufacturing, making the design of effective and efficient scheduling algorithms complicated. In this paper, we analyzed the AGV dispatching tasks in the workshop and model the workshop as a node network, and apply an improved Multi-Agent Reinforcement Learning (MARL), that is, an improved QMIX model to solve the AGV scheduling problem. The experiment results show that the proposed approach outperforms the other commonly-used methods such as deep reinforcement learning (DQN) under different environment settings, in term of the maximum makespan of AGV.

Inspired by the traditional art of paper folding, origami, autonomous production of 3D structures from 2D sheets can be achieved by the implementation of self-folding techniques. One technique to achieve such transformation is the usage of thermo-responsive smart materials such as self-folding polymeric films, which can be controlled by heat to shrink. Achieving remote self-folding with a practical approach remains a major challenge due to the requirement for specific environments, or having to accompany electronics on origami, which limits the complexity of the origami design. In this paper, we present a wireless method to trigger the thermo-responsive self-folding process of the origami robots through magnetic induction. The proposed method is applicable for all electrically conductive materials and can wirelessly fold a mobile origami robot with a size of 32 × 30 mm2. This method eliminates the need for inclusion of electronics on the origami or usage of complicated trigger methods and environmental conditions, allowing the robot to fold in a wider range of applications such as in constrained spaces.

This paper considers deep visual recognition on long-tailed data. Under the long-tailed distribution, a small portion of the classes (head classes) occupy most training samples and the most classes (tail classes) only occupy relatively few samples. We observe that such long-tailed distribution significantly distorts the deeply-learned feature space, which consequentially compromises the deep visual recognition. Specifically, during training, each head class is prone to a relatively wide spatial distribution in the deep feature space, while each tail class is prone to a relatively small spatial distribution. In another word, the tail classes usually have much smaller spatial distribution than the head classes, distorting the overall feature space. In response, we propose to explicitly inflate the distribution of each tail class in the deep feature space, so that the tail classes will have comparable distribution range as the head classes. To this end, we replace each tail feature vector with a set of feature vectors on the fly. These feature vectors follow a probabilistic distribution learned from the head classes and yield a “feature cloud” surrounding the original tail feature. We show that the feature cloud effectively transfers the within-class diversity from the head classes onto the tail classes, maintaining an effect of probabilistic feature augmentation. An important advantage of the proposed feature cloud is that it is capable to bring general improvement to long-tailed visual recognition on two fundamental tasks,i.e., deep classification and deep representation learning, in spite of the significant differences between them. Extensive experiments on both deep metric learning benchmarks and deep image classification benchmarks validate the effectiveness of the proposed feature cloud.

Unrestricted file upload vulnerabilities enable attackers to upload malicious scripts to a web server for later execution. We have built a system, namely UFuzzer, to effectively and automatically detect such vulnerabilities in PHP-based server-side web programs. Different from existing detection methods that use either static program analysis or fuzzing, UFuzzer integrates both (i.e., static-fuzzing co-analysis). Specifically, it leverages static program analysis to generate executable code templates that compactly and effectively summarize the vulnerability-relevant semantics of a server-side web application. UFuzzer then “fuzzes” these templates in a local, native PHP runtime environment for vulnerability detection. Compared to static-analysis-based methods, UFuzzer preserves the semantics of an analyzed program more effectively, resulting in higher detection performance. Different from fuzzing-based methods, UFuzzer exercises each generated code template locally, thereby reducing the analysis overhead and meanwhile eliminating the need of operating web services. Experiments using real-world data have demonstrated that UFuzzer outperforms existing methods in either efficiency, or accuracy, or both. In addition, it has detected 31 unknown vulnerable PHP scripts including 5 CVEs.

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have a significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. In response, we seek to expand the distribution of the tail classes during training, so as to alleviate the distortion of the feature space. To this end, we propose to augment each instance of the tail classes with certain disturbances in the deep feature space. With the augmentation, a specified feature vector becomes a set of probable features scattered around itself, which is analogical to an atomic nucleus surrounded by the electron cloud. Intuitively, we name it as ``feature cloud''. The intra-class distribution of the feature cloud is learned from the head classes, and thus provides higher intra-class variation to the tail classes. Consequentially, it alleviates the distortion of the learned feature space, and improves deep representation learning on long tailed data. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.

A button battery accidentally ingested by a toddler or small child can cause severe damage to the stomach within a short period of time. Once a battery lands on the surface of the esophagus or stomach, it can run a current in the tissue and induce a chemical reaction resulting in injury. Following our previous work where we presented an ingestible magnetic robot for button battery retrieval, this study presents a remotely achieved novel localization method of a button battery with commonly available magnetic sensors (Hall-effect sensors). By applying a direct magnetic field to the button battery using an electromagnetic coil, the battery is magnetized, and hence it becomes able to be sensed by Hall-effect sensors. Using a trilateration method, we were able to detect the locations of an LR44 button battery and other ferromagnetic materials at variable distances. Additional four electromagnetic coils were used to autonomously navigate a magnet-containing capsule to dislocate the battery from the affected site.