--This paper reports the development of a new open-access modular framework, called Marine V ehicle Packages (MVP), for Autonomous Underwater V ehicles. The framework consists of both software and hardware designs allowing easy construction of AUV for research with increased customizability and sufficient payload capacity. This paper will present the scalable hardware system design and the modular software design architecture. New features, such as articulated thruster integration and high-level Graphic User Interface will be discussed. Both simulation and field experiments results are shown to highlight the performance and compatibility of the MVP . Autonomous underwater vehicle is a growing area since they are great tools for ocean research and defense purposes. Commercial-off-the-shelf (COTS) AUVs are supplied with proprietary software are great when they are used as an equipment for collecting scientific data, e.g., survey the seabed and profile the water column.

In 2024, the "low-altitude economy" was written into China's Government Work Report for the first time [1], and flying robots have been rapidly popularized nationwide. From an environmental perspective, electrically powered air vehicles are attracting growing attention; key technologies include overall configuration design, integrated energy management, and high-efficiency, high power-to-weight electric propulsion [2]. For electric propulsion, mainstream systems use electric motors to drive propellers, but propeller noise is significant and hard to mitigate [3], which limits widespread use in cities--the main arena for the low-altitude economy--and is also unfavorable for silent reconnaissance. Hence, there is a pressing need for a new propulsion approach enabling quiet, fully electric flight. In the 1920s, Brown observed that an asymmetric capacitor under high voltage can generate thrust, known as the Biefeld-Brown effect. A leading explanation is ionic wind: a high electric field ionizes air, and the resulting ions accelerate and transfer momentum to neutral molecules, producing a net airflow (thrust) [4]. Xu et al. first mounted a plasma thruster on a fixed-wing UAV without other propulsion; the gliding distance with the thruster on was five times that with it off, but the maximum range was only 45m and no controller design was provided [5]. Zhang et al. realized altitude control for a micro ionic-wind-powered UA V using passive components, but the wingspan was at most 6 .3cm

The global demand for aquaculture has surged over the past decade, driving the expansion of offshore fish farming systems such as net pens [1, 2]. These structures, while effective for large-scale fish production, are continuously exposed to harsh marine environments that can degrade structural integrity, compromise biosecurity, and increase the risk of fish escape or environmental contamination [3]. As a result, regular and reliable inspection of aquaculture net pens is critical to ensuring operational safety, productivity, and regulatory compliance [4]. Recent advances in underwater robotics, control systems, and computer vision have enabled significant progress in autonomous inspection [5, 6]. Remotely Operated Vehicles (ROVs), in particular, offer a practical platform for deploying sensing payloads such as cameras, sonars and performing close-range inspection in confined underwater environments [7]. However, most existing ROV-based systems operate in isolation, with limited autonomy and minimal adaptability to dynamic conditions such as power constraints, actuator degradation, and evolving mission demands [8, 9]. Moreover, mission planning and coordination typically require expert operators, limiting the scalability and responsiveness of these systems in real-world aquaculture operations [10, 11, 12]. To address these challenges, we propose AquaChat++, a novel framework that combines the reasoning capabilities of Large Language Models (LLMs) with multi-ROV coordination, battery-aware mission planning, and fault-tolerant control [13, 14]. Unlike traditional inspection pipelines that rely on fixed scripts or manual supervision, AquaChat++ enables natural language-driven task planning and dynamic allocation across multiple ROVs.

Bayesian inference is applied to calibrate and quantify prediction uncertainty in a coupled multi-component Hall thruster model at varying facility background pressures. The model, consisting of a cathode model, discharge model, and plume model, is used to simulate two thrusters across a range of background pressures in multiple vacuum test facilities. The model outputs include thruster performance metrics, one-dimensional plasma properties, and the angular distribution of the current density in the plume. The simulated thrusters include a magnetically shielded thruster, the H9, and an unshielded thruster, the SPT-100. After calibration, the model captures several key performance trends with background pressure, including changes in thrust and upstream shifts in the ion acceleration region. Furthermore, the model exhibits predictive accuracy to within 10\% when evaluated on flow rates and pressures not included in the training data, and the model can predict some performance characteristics across test facilities to within the same range. Evaluated on the same data as prior work [Eckels et al. 2024], the model reduced predictive errors in thrust and discharge current by greater than 50%. An extrapolation to on-orbit performance is performed with an error of 9\%, capturing trends in discharge current but not thrust. Possible extensions and improvements are discussed in the context of using data for predictive Hall thruster modeling across vacuum facilities.

The increasing demand for underwater exploration and rescue operations enforces the development of advanced wireless or semi-wireless underwater vessels equipped with manipulator arms. This paper presents the implementation of a semi-wireless underwater vehicle, "TritonZ" equipped with a manipulator arm, tailored for effective underwater exploration and rescue operations. The vehicle's compact design enables deployment in different submarine surroundings, addressing the need for wireless systems capable of navigating challenging underwater terrains. The manipulator arm can interact with the environment, allowing the robot to perform sophisticated tasks during exploration and rescue missions in emergency situations. TritonZ is equipped with various sensors such as Pi-Camera, Humidity, and Temperature sensors to send real-time environmental data. Our underwater vehicle controlled using a customized remote controller can navigate efficiently in the water where Pi-Camera enables live streaming of the surroundings. Motion control and video capture are performed simultaneously using this camera. The manipulator arm is designed to perform various tasks, similar to grasping, manipulating, and collecting underwater objects. Experimental results shows the efficacy of the proposed remotely operated vehicle in performing a variety of underwater exploration and rescue tasks. Additionally, the results show that TritonZ can maintain an average of 13.5cm/s with a minimal delay of 2-3 seconds. Furthermore, the vehicle can sustain waves underwater by maintaining its position as well as average velocity. The full project details and source code can be accessed at this link: https://github.com/kawser-ahmed-byte/TritonZ

Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement simultaneously. In this paper, we propose a novel multimodal surfing aquatic-aerial vehicle, SurfAA V, which efficiently integrates underwater navigation, surface gliding, and aerial flying capabilities. Thanks to the design of the novel differential thrust vectoring hydrofoil, SurfAA V can achieve efficient surface gliding and underwater navigation without the need for a buoyancy adjustment system. This design provides flexible operational capabilities for both surface and underwater tasks, enabling the robot to quickly carry out underwater monitoring activities. Additionally, when it is necessary to reach another water body, SurfAA V can switch to aerial mode through a gliding takeoff, flying to the target water area to perform corresponding tasks. The main contribution of this letter lies in proposing a new solution for underwater, surface, and aerial movement, designing a novel hybrid prototype concept, developing the required control laws, and validating the robot's ability to successfully perform surface gliding and gliding takeoff. SurfAA V achieves a maximum surface gliding speed of 7.96 m/s and a maximum underwater speed of 3.1 m/s. The prototype's surface gliding maneuverability and underwater cruising maneuverability both exceed those of existing aquatic-aerial vehicles. N recent years, with the rapid development of robotics technology, unmanned aquatic-aerial vehicles(UAA Vs) capable of adapting to complex environments and performing diversified tasks have gradually become a research hotspot. These robots integrate the advantages of both autonomous underwater vehicles(AUVs) and unmanned aerial vehicles(UA Vs), allowing them to freely switch between motion modes in water and air. This capability greatly broadens the application scope of traditional robots, demonstrating enormous potential in multi-domain missions such as environmental monitoring[1], disaster rescue[2], and national defense[3].

This paper introduces an underwater multi-robot platform, named Aucamp, characterized by cost-effective monocular-camera-based sensing, distributed protocol and robust orientation control for localization. We utilize the clarity feature to measure the distance, present the monocular imaging model, and estimate the position of the target object. We achieve global positioning in our platform by designing a distributed update protocol. The distributed algorithm enables the perception process to simultaneously cover a broader range, and greatly improves the accuracy and robustness of the positioning. Moreover, the explicit dynamics model of the robot in our platform is obtained, based on which, we propose a robust orientation control framework. The control system ensures that the platform maintains a balanced posture for each robot, thereby ensuring the stability of the localization system. The platform can swiftly recover from an forced unstable state to a stable horizontal posture. Additionally, we conduct extensive experiments and application scenarios to evaluate the performance of our platform. The proposed new platform may provide support for extensive marine exploration by underwater sensor networks.

--The underwater Unmanned V ehicles(UUVs) are pivot tools for offshore engineering and oceanographic research. Most existing UUVs do not facilitate easy integration of new or upgraded sensors. A solution to this problem is to have a modular UUV system with changeable payload sections capable of carrying different sensor to suite different missions. The design and implementation of a modular and decentral UUV named Y ellowCube is presented in the paper . Instead a centralised software architecture which is adopted by the other modular underwater vehicles designs, a Peer-T o-Peer(P2P) communication mechanism is implemented among the UUV's modules. The experiments in the laboratory and sea trials have been executed to verify the performances of the UUV . Over the past few decades, the Unmanned Underwater V ehicles(UUVs) have become the essential tools in the offshore engineering and the ocean research. Their tasks ranges from the offshore engineering, oceanographic research, salvage and rescue to the military monitoring.

Hall Effect Thrusters (HETs) are electric thrusters that eject heavy ionized gas particles from the spacecraft to generate thrust. Although traditionally they were used for station keeping, recently They have been used for interplanetary space missions due to their high delta-V potential and their operational longevity in contrast to other thrusters, e.g., chemical. However, the operation of HETs involves complex processes such as ionization of gases, strong magnetic fields, and complicated solar panel power supply interactions. Therefore, their operation is extremely difficult to model thus necessitating Data Assimilation (DA) approaches for estimating and predicting their operational states. Because HET's operating environment is often noisy with non-Gaussian sources, this significantly limits applicable DA tools. We describe a topological approach for data assimilation that bypasses these limitations that does not depend on the noise model, and utilize it to forecast spatiotemporal plume field states of HETs. Our approach is a generalization of the Topological Approach for Data Assimilation (TADA) method that allows including different forecast functions. We show how TADA can be combined with the Long Short-Term Memory network for accurate forecasting. We then apply our approach to high-fidelity Hall Effect Thruster (HET) simulation data from the Air Force Research Laboratory (AFRL) rocket propulsion division where we demonstrate the forecast resiliency of TADA on noise contaminated, high-dimensional data.

We present MeCO, the Medium Cost Open-source autonomous underwater vehicle (AUV), a versatile autonomous vehicle designed to support research and development in underwater human-robot interaction (UHRI) and marine robotics in general. An inexpensive platform to build compared to similarly-capable AUVs, the MeCO design and software are released under open-source licenses, making it a cost effective, extensible, and open platform. It is equipped with UHRI-focused systems, such as front and side facing displays, light-based communication devices, a transducer for acoustic interaction, and stereo vision, in addition to typical AUV sensing and actuation components. Additionally, MeCO is capable of real-time deep learning inference using the latest edge computing devices, while maintaining low-latency, closed-loop control through high-performance microcontrollers. MeCO is designed from the ground up for modularity in internal electronics, external payloads, and software architecture, exploiting open-source robotics and containerarization tools. We demonstrate the diverse capabilities of MeCO through simulated, closed-water, and open-water experiments. All resources necessary to build and run MeCO, including software and hardware design, have been made publicly available.