As marine exploration becomes increasingly important, marine robots have been extensively studied in recent years. Despite some well-designed robots have already achieved to various successful missions, most existing robots struggle to adapt to diverse demands or tasks due to their fixed structure and complexity of the marine environment. To address these challenges, we present a novel reconfigurable marine robot named Sea-U-Whale. This system can dynamically adjust its actuator configuration in the marine environment, providing superior environmental adaptability, maneuverability, and ver-satile mobility. Considering the demands of unmanned ocean exploration, an active reconfiguration mechanism and three distinct vehicle modes are designed for optimal actuation in various marine scenarios. The multi-modal mobility of our system and its robust performance have been validated through extensive field tests and water tank experiments, demonstrating its potential in handling a wide range of mission profiles.

Coupling-Tiltable Unmanned Aerial-Aquatic Vehicles (UAAVs) have gained increasing importance, yet lack comprehensive analysis and suitable controllers. This paper analyzes the underwater motion characteristics of a self-designed UAAV, Mirs-Alioth, and designs a controller for it. The effectiveness of the controller is validated through experiments. The singularities of Mirs-Alioth are derived as Singular Thrust Tilt Angle (STTA), which serve as an essential tool for an analysis of its underwater motion characteristics. The analysis reveals several key factors for designing the controller. These include the need for logic switching, using a Nussbaum function to compensate control direction uncertainty in the auxiliary channel, and employing an auxiliary controller to mitigate coupling effects. Based on these key points, a control scheme is designed. It consists of a controller that regulates the thrust tilt angle to the singular value, an auxiliary controller incorporating a Saturated Nussbaum function, and a logic switch. Eventually, two sets of experiments are conducted to validate the effectiveness of the controller and demonstrate the necessity of the Nussbaum function.

Defect diagnosis in urban infrastructure is crucial for public safety. Traditional manual inspections face significant challenges in terms of accuracy and cost-effectiveness. In this paper, we propose a lightweight and hardware-friendly large-scale infrastructure detector, CUPID, highly suitable for unmanned aerial vehicles (UAVs). Given the significant challenges in automatically detecting defects of varying intensity and size within complex infrastructure, along with the tendency of lightweight models to lose detail and fail to fully capture features during the defect extraction process, we propose the CUPID_Block, a multi-level information fusion block to construct the backbone, featuring the CUPID_Conv module equipped with our proposed CCA (CrissCross Attention). Furthermore, CUPID features an auxiliary training branch that assimilates lower feature maps, helping to recover details lost in deeper convolutional layers. To verify the effectiveness of CUPID and to address the lack of a suitable dataset in the community, we establish a multi-scenario infrastructure defect dataset, CUBIT2024, to conduct extensive experiments. Finally, to assess the efficiency and adaptability of CUPID in UAV for online infrastructure inspection, we design a compact autonomous drone, CU-Astro, where the proposed CUPID is deployed on the Jetson Orin NX computer onboard to evaluate the speed and power consumption of the inference.

Spatial reasoning is a fundamental capability of multimodal large language models (MLLMs), yet their performance in open aerial environments remains underexplored. In this work, we present Open3D-VQA, a novel benchmark for evaluating MLLMs' ability to reason about complex spatial relationships from an aerial perspective. The benchmark comprises 73k QA pairs across seven general spatial reasoning tasks, offered in multiple-choice, true/false, and short-answer formats, and supports both visual and point cloud modalities. The questions are automatically generated from spatial relations extracted from both real-world and simulated aerial scenes. Evaluation on 13 popular MLLMs reveals that: 1) Models are generally better at answering questions about relative spatial relations than absolute distances, 2) 3D LLMs fail to demonstrate significant advantages over 2D LLMs, and 3) Fine-tuning solely on the simulated dataset can significantly improve the model's spatial reasoning performance in real-world scenarios. The benchmark, generation pipeline, and evaluation toolkit are released on this page.

In recent years, significant progress has been made in the field of underwater image enhancement (UIE). However, its practical utility for high-level vision tasks, such as underwater object detection (UOD) in Autonomous Underwater Vehicles (AUVs), remains relatively unexplored. It may be attributed to several factors: (1) Existing methods typically employ UIE as a pre-processing step, which inevitably introduces considerable computational overhead and latency. (2) The process of enhancing images prior to training object detectors may not necessarily yield performance improvements. (3) The complex underwater environments can induce significant domain shifts across different scenarios, seriously deteriorating the UOD performance. To address these challenges, we introduce EnYOLO, an integrated real-time framework designed for simultaneous UIE and UOD with domain-adaptation capability. Specifically, both the UIE and UOD task heads share the same network backbone and utilize a lightweight design. Furthermore, to ensure balanced training for both tasks, we present a multi-stage training strategy aimed at consistently enhancing their performance. Additionally, we propose a novel domain-adaptation strategy to align feature embeddings originating from diverse underwater environments. Comprehensive experiments demonstrate that our framework not only achieves state-of-the-art (SOTA) performance in both UIE and UOD tasks, but also shows superior adaptability when applied to different underwater scenarios. Our efficiency analysis further highlights the substantial potential of our framework for onboard deployment.

Autonomous Marine Vehicles (AMVs) have been widely used in many critical tasks such as surveillance, patrolling, marine environment monitoring, and hydrographic surveying. However, most typical AMVs cannot meet the diverse demands of different marine tasks. In this article, we design a new type of remote-controlled hydrofoil marine vehicle, named Sea-U-Foil, which is suitable for different marine scenarios. Sea-U-Foil features three distinct locomotion modes, displacement mode, foilborne mode, and submarine mode, which enable the platform flexible mobility, high-speed and high-load capacities, and superior concealment. Specifically, the submarine mode makes Sea-U-Foil unique among previous studies. In addition, the performance of Sea-U-Foil in foilborne mode outperforms those of most current unmanned surface vehicles (USVs) in terms of speed and payload. To the best of our knowledge, we are the first to introduce a new type of AMV that can work in displacement mode, foilborne mode, and submarine mode. We elaborate on the design principles and methodologies of Sea-U-Foil first, then validate the effectiveness of its tri-modal locomotion through extensive experiments.

Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air. For propulsion, the operating range is switched for the different mediums by the dual-speed propulsion unit, providing sufficient thrust and also ensuring output efficiency. For thruster configuration, thrust vectoring is realized by the rotation of the propulsion unit around the mount arm, thus enhancing the underwater maneuverability. This paper presents a quadrotor prototype of this concept and the design details and realization in practice.