--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.

Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwater. In this paper, we present a tightly-coupled multi-sensors fusion framework to increase localization accuracy that is robust to sensor failure. Visual images, Doppler Velocity Log (DVL), Inertial Measurement Unit (IMU) and Pressure sensor are integrated into the state-of-art Multi-State Constraint Kalman Filter (MSCKF) for state estimation. Besides that a new keyframe-based state clone mechanism and a new DVL-aided feature enhancement are presented to further improve the localization performance. The proposed method is validated with a data set collected in the field under frozen ice, and the result is compared with 6 other different sensor fusion setups. Overall, the result with the keyframe enabled and DVL-aided feature enhancement yields the best performance with a Root-mean-square error of less than 2 m compared to the ground truth path with a total traveling distance of about 200 m.

Factor analysis or sometimes referred to as variable analysis has been extensively used in classification problems for identifying specific factors that are significant to particular classes. This type of analysis has been widely used in application such as customer segmentation, medical research, network traffic, image, and video classification. Today, factor analysis is prominently being used in fault diagnosis of machines to identify the significant factors and to study the root cause of a specific machine fault. The advantage of performing factor analysis in machine maintenance is to perform prescriptive analysis (helps answer what actions to take?) and preemptive analysis (helps answer how to eliminate the failure mode?). In this paper, a real case of an industrial rotating machine was considered where vibration and ambient temperature data was collected for monitoring the health of the machine. Gaussian mixture model-based clustering was used to cluster the data into significant groups, and spectrum analysis was used to diagnose each cluster to a specific state of the machine. The significant features that attribute to a particular mode of the machine were identified by using the random forest classification model. The significant features for specific modes of the machine were used to conclude that the clusters generated are distinct and have a unique set of significant features.