Sea state estimation is a fundamental problem in the development of autonomous ships. Traditional methods such as wave buoy, satellites, and wave radars are limited by locations, clouds and costs, respectively. Model-based methods are prone to incorrect estimations due to their high dependency on mathematical models of ships. As previous data-driven studies for sea state estimation only consider wave height and use the motion data from dynamic positioning vessels, this paper introduces a new, deep neural network (SSENET) to estimate sea state in light of both wave height and wave direction, and extends the generality of sensor data from ship motion with forward speed. SSENET is built on the basis of stacked convolutional neural network blocks with dense connections between different blocks, channel attention modules and a feature attention module. The dense connections build shortcut paths between input and all subsequent convolutional blocks, which can make full use of all the hierarchical features from the original time series sensor data. The channel attention modules aim to enhance the features extracted by each convolution block. The feature attention module focuses on combining the feature fusion of hierarchical features in an adaptive manner. Benchmark experiments show the competitive performance against state-ofthe-art approaches. Applying the SSENET on two datasets of zigzag motion for comparative studies shows the effectiveness of the proposed method.

Developing a reliable model to identify the sea state is significant for the autonomous ship. This paper introduces a novel deep neural network model (SeaStateNet) to estimate the sea state based on the ship motion data from dynamically positioned vessels. The SeaStateNet mainly consists of three components: an Long-Short-Term Memory (LSTM) recurrent neural network to capture the long dependency in the ship motion data; a convolutional neural network (CNN) to extract time-invariant features; and a Fast Fourier Transform (FFT) block to extract frequency features. A feature fusion layer is designed to learn the degree affected by each component. The proposed model is applied directly to the raw time series data, without needing of any hand-engineered features. A sensitivity analysis (SA) method is applied to assess the influence of data preprocessing. Through benchmark test and experiment on ship motion dataset, SeaStateNet is verified effective for sea state estimation. The investigation on real-time test further shows the practicality of the proposed model.

On modern mechatronic products, incorporating multiple modes is a common and effective way of dealing with changes in task, requirements, and environment. Modes are established to enable the system to switch from one configuration state to another. However, using the traditional methodology in engineering design, products are considered and designed with fixed configurations. A systematic method to involve and enable the design of changeable configurations is lacking. This paper focuses on product functional models and investigates the conceptual design of multi-modal products, which are identified by their reconfigurability during the operation stage. The author connects the phenomenon of multiple modes to product reconfigurability, asserts function and technology multiplications as the basis of multiple modes, and then specifies that usability and robustness are the key drivers of incorporating multiple modes. At the end of the paper, the author reconciles the conceptual design procedures to derive the principle solutions specifically for multi-modal products. This research on the dynamic characteristics of the product functional model introduced by multiple modes complements the current systematic design methodology.

Features of fish like environmental compatibility and maneuverability have attracted bio-inspired design researchers worldwide to contemplate the practical applications of robotic fish. This paper presents a conceptual design and development of a robotic fish based on the cownose ray. We extracted essential biomimetic parameters of the cownose ray to develop reasonable simplifications of the body shape, the mechanical structure design principle of the multi-joint driving fin rays, and the motion principle. Practical motion abilities of the internal driven skeleton of the bionic prototype are calculated theoretically and compared with its natural model. Parameters affecting propulsion performances are analyzed utilizing a one-dimensional calculation method. The basic motion modes are obtained according to the analysis. Observations show that the developed robotic fish can perform bionic sinusoidal flapping movements. Positive forward propulsion forces and desired turn torques are measured on the towing tank. The maximum linear forward swimming speed of the bionic fish is 0.7 times of body length per second (BL/s). Maneuvering abilities of the pivot turn and swimming through narrow passages by rolling swim are demonstrated to confirm the design idea.

Abstract-This paper focuses on fast plane detection in noisy range images. First, two improvements to the state-of-the-art region growing algorithm are presented to make it faster without losing precision for unstructured environments. One is to add the seed selection procedure based on local shape information to avoid blind growth. The other is to simplify the plane fitting mean square error computation complex. Second, a novel algorithm called grid-based region growing is presented for structured environments. The point cloud is divided into small patches based on neighborhood information when it is viewed as a range image. The small patch is called grid. Then the grids are classified into different categories according to their local appearance, including sparse, planar, spherical and linear. Finally, the planar grids are clustered into big patches by region growing. The plane parameters are incrementally computed whenever a new grid is added. The resulting planes can be used for 3D plane simultaneous localization and mapping (SLAM). Experimental results show promising plane detecting speed for both structured and unstructured environments.