Underwater salient object detection (USOD) is an emerging research area that has great potential for various underwater visual tasks. However, USOD research is still in its early stage due to the lack of large-scale datasets within which salient objects are well-defined and pixel-wise annotated. To address this issue, this paper introduces a new dataset named USOD10K. It contains 10,255 underwater images, covering 70 categories of salient objects in 12 different underwater scenes. Moreover, the USOD10K provides salient object boundaries and depth maps of all images. The USOD10K is the first large-scale dataset in the USOD community, making a significant leap in diversity, complexity, and scalability. Secondly, a simple but strong baseline termed TC-USOD is proposed for the USOD10K. The TC-USOD adopts a hybrid architecture based on an encoder-decoder design that leverages transformer and convolution as the basic computational building block of the encoder and decoder, respectively. Thirdly, we make a comprehensive summarization of 35 state-of-the-art SOD/USOD methods and benchmark them on the existing USOD dataset and the USOD10K. The results show that our TC-USOD achieves superior performance on all datasets tested. Finally, several other use cases of the USOD10K are discussed, and future directions of USOD research are pointed out. This work will promote the development of the USOD research and facilitate further research on underwater visual tasks and visually-guided underwater robots. To pave the road in the USOD research field, the dataset, code, and benchmark results are publicly available: https://github.com/Underwater-Robotic-Lab/USOD10K.

Cable-driven continuum manipulators have gained considerable attention due to their high dexterity and inherent structural compliance, making them a popular research topic. However, previous studies have overlooked the kinetostatics of these manipulators, which can result in a multi-solution problem. This issue is critical as having multiple equilibrium states can lead to erroneous estimations of the manipulator's profile. To address this issue, the kinetostatic model is presented and simulations based on both the interval analysis method and the commonly used floating-point optimization algorithm are conducted under the same actuating forces and external loads. Results show that there are multiple solutions to the kinetostatics of cable-driven continuum manipulators with constant cross section or variable cross section. This paper fills a gap in the current literature and offers valuable insights for researchers in the field of cable-driven continuum manipulators.

Underwater optical wireless communication (UOWC) networks are promising for many civilian and industrial applications due to underwater high-speed data transmission demand. This paper focuses on the distributed time division multiple access (TDMA) protocol design for UOWC networks. Unlike existing research, we focus on a more general underwater communication scenario where nodes are scattered, and node mobility is considered. We propose a distributed TDMA-based medium access control(MAC) protocol, called cluster-based cross-layer multi-slot MAC(CCM-MAC), which can assign multiple slots to each node according to the slot-occupying information. When using CCM-MAC, the network is divided into clusters. Each node maintains knowledge of the cluster topology and slot-occupying information of surrounding nodes through routing techniques; by updating this knowledge, collisions can also be detected and eliminated dynamically. Analysis results are presented to evaluate the performance of CCM-MAC, and simulations show that high network throughput and low collision rate can be realized in comparison with existing contention-based MAC protocols.

A vision-based head tracking method for robotic transcranial magnetic stimulation (TMS), requiring no attachments to patient, is proposed to track the movements of patient's head during treatment. First, a facial landmark detection method based on Constrained Local Neural Field (CLNF) algorithm is used. Second, a method of head location and pose estimation is presented and a filter for image restoration is used for a higher accuracy. Finally, calibration approaches are proposed based on the coordinates of the robot arm and camera and real-time motion tracking system. Experiment results indicate that the accuracy and real-time performance of this head tracking method satisfy the requirements of TMS treatment.

The multi-robot formation is a key technology for underwater searching and other tasks. This paper aims to use underactuated underwater robots to establish a leader-follower formation model. Firstly, the kinematics and dynamics characteristics of the single AUV are analyzed and the coordinate transformation is introduced to decouple the dynamics of the AUV. Then the leader-follower model is obtained and the linear feedback controller is proposed, and the Lyapunov direct method is applied in the stability analysis. The simulation experimental results show that the formation of two AUVs was controlled effectively by the leader-follower formation model. We also design hardware platform to verify the reliability of the simulation. Moreover, the experimental results of expanding from two AUVs formation to three AUVs formation also prove the applicability of the method for the multi-AUVs system.

Drowsiness driving poses a huge threat to the traffic safety. In this paper, a novel drowsiness driving detection method based on multi-feature fusion and long short-term memory (LSTM) recurrent neural networks is proposed to reduce traffic accidents caused by drowsiness driving. Firstly, we collect steering wheel angles (SWAs) of vehicles and facial videos of drivers by a driving simulator. Secondly, the drowsiness driving-related steering features and facial expression features are respectively extracted from the collected SWAs and facial videos by the One Way ANOVA method and the FEFENet network, and then they are fused by concatenation operation. Considering that the generation of drowsiness is a long-term dynamic process and the degree of drowsiness accumulates over time, we design LSTM networks to cope with the fused feature sequence in a fixed duration, thereby establishing a effective drowsiness driving detection model. Some experiments are conducted to validate the performance of the proposed method, and the results demonstrate that our method can get robust and high accuracy performance in many challenging driving scenarios.

This paper introduces a new paradigm that combines in-swarm sampling/processing together with formation forming methods. The introduced concept is capable of generating a regular sampling lattice through emergent behavior of swarm agents and, in a second step, exploits this formation using 2-D filtering and processing algorithms based on the so-called multidimensional Roesser model. The swarm system scales well and the low complexity approach requires minimal hardware. Formation control is achieved through artificial potential fields generated globally by a few higher tier agents and locally by the massive amount of lower tier agents. The paper illustrates the proposed methods by applying line forming to a swarm example that scans an unknown area while performing in-swarm distributed filtering with subsequent marking of potential target areas. For the first time in the literature, a modified distributed 2-D Roesser model is combined with formation forming in order to achieve the desired results.

A method for arbitrary surface reconstruction from 3D large scattered points is proposed in this paper. According to the properties of 3D points, e.g. the non-uniform distribution and unknown topology, an implicit surface model is adopted based on radial basis functions network. And because of the property of locality of radial basis function, the method is fast and robust in surface reconstruction. Furthermore, an adapted K-Means algorithm is used to reduce reconstruction centers. For features completeness, two effective methods are introduced to extract the feature points before the adapted K-Means algorithm. Experiment results show that the presented approach is a good solution for reconstruction from 3D large scattered points.