Sailboats are purely wind-driven and thus have great potential for long-term voyaging. For robotic sailboats, the constraints on the energy of the control boards, sensors, communication modules, and actuators are crucial to the sustainability of automation. Reducing the control frequency of actuators is crucial for energy conservation. This study proposes an energy-efficient long-short term (EeLsT) approach for sustainable sailing. In EeLsT, long-term and short-term observers are designed to adaptively take control decisions for time-varying environmental influences (e.g., waves and currents). Our approach can be generally applied as an energy management module in sailing robots. It explicitly leverages the sailing motion characteristics and the dynamic model of the robot considering marine disturbances. We have designed an experimental enhanced simulation platform to evaluate motion performance and energy consumption. Both baseline approach and the scheme incorporating EeLsT method (refer to as EeLsT approach in the subsequent sections) have been conducted. In simulation, EeLsT approach saves 31.8% energy. In the real marine environment, experiments are conducted with OceanVoy, a catamaran sailing robot. The results show that 27.4% of the energy is saved during stable sailing. In long-term sailing, compared to the standby mode when the motors are not working, the average power of the full automation mode has increased by no more than 1 W, i.e. 4% relatively.

This paper presents a strategy for predicting the turning radius of a sailing robot with consideration of aerodynamic and hydrodynamic interferences from the marine environment. The turning radius is initially obtained based on three consecutive designated points during the turning process, which is regarded as the baseline method. Subsequently, on the basis of our constructed turning datasets, a model is trained using Gaussian process regression (GPR) to achieve radius prediction. The feasibility and effectiveness of the proposed scheme have been validated in both simulation and experiments (conducted with OceanVoy as shown in Fig. 1). Under experimental circumstances, the Mean Absolute Error (MAE) of the turning radius produced by the trained prediction model is 0.58m. Furthermore, it has been observed that during longterm sailing covering a distance of 1200km, apart from wind speed and robot velocity, the tidal range also has a significant impact on the navigation of sailing robots.

To effectively solve the problem of a small proportion of substantia nigra and midbrain regions in Magnetic Resolution Imaging (MRI) images of Parkinson’s disease (PD) patients, unclear boundaries with surrounding tissues, and difficulty in accurately delineating boundaries, an improved U2-Net algorithm for Parkinson’s substantia nigra midbrain image segmentation was proposed. This algorithm first improves the Residual U-blocks (RSU) and RSU-4F modules using the Shuffle Attention (SA) module, enhancing the network’s attention to Parkinson’s substantia nigra and midbrain blurry regions in the encoding and decoding layers. Next, replace some ordinary convolutions in RSU-4F with DynamicConv (DyConv), capture local features through dynamic convolution kernels, and improve the segmentation performance of Parkinson’s images. The experimental data used clinical data, and the improved algorithm achieved Dice, Precision, Recall, and mIou of 80.27%, 89.99%, 88.88%, and 82.38% in substantia nigra segmentation, respectively. The experimental results show that this algorithm can achieve more precise segmentation of Parkinson’s images.

Ocean observation is an emerging field, and sailing robots have several promising features (e.g., long-range sailing, environmental friendliness, energy-saving and low-noise) to perform tasks. In this paper, we define an ocean observation mission in a restricted target area as a station keeping problem. Inspired by an orientation-restricted Dubins path method, the robot keeps sailing and collecting data in a smooth reciprocation, where the trajectories consist of sailing against wind segments and turning downwind parts divided by a goal area and an acceptable area. The upwind sailing segments are of interest for data acquisition. However, the system stability can not be guaranteed during the whole reciprocation especially for sailing outside the goal area. Hereby, we refer to a switched systems approach and propose a desired heading generation scheme to realize safe and stable control in both areas. The stability for subsystems is proved with Lyapunov-like functions. The stable station keeping scheme is verified in both simulation and real experiments. Finally, we completed continuous and effective observation within 50 minutes in the goal area with a radius of 50 meters by a catamaran robot named OceanVoy460.

Obstacle avoidance is crucial for autonomous surface vehicles (ASVs) in the sea because rescue is extremely difficult there. OceanVoy, a sailboat toward long range energy-saving voyage, has to overcome the dual challenges, i.e. from the environmental interference and its low mobility preventing from precise obstacle avoidance. We propose a control scheme based on real-time collision risk assessment and a hybrid propulsion system to enhance safety of OceanVoy. A novel sailboat safety zone (SSZ) has been designed to warn the potential collision during its sailing. Both intrinsic characteristics of OceanVoy and environmental factors have been considered in SSZ. We use lateral and axial thrusters to provide emergency propulsion. A collision avoidance algorithm is executed to coordinate motors in rudder, sail and thrusters based on SSZ. Both simulation and experiments have been conducted and the results have validated our system and collision avoidance scheme.

This paper proposes a drive-through recharging strategy for a quadrotor. First, a ground charging station is constructed with a portable charging wire, then with two conductive hooks connected to the battery, the quadrotor is flying through the station. Finally, after connecting the hooks to the charging wire, the quadrotor can be recharged without landing or stopping, similar to a drive-through. During the charging, the quadrotor is still able to accomplish local monitoring assignments. The main challenge of this work is to ensure that the hooks successfully connect to the charging wire. Though visual-based techniques appear promising to handle this problem, the detection of a thin charging wire is challenging especially during the flight. Besides, the conductive hooks underneath the quadrotor are out of the camera view, which brings more challenges to the problem. Therefore, in this paper, the aiming problem is directed into the position constraint control problem of the quadrotor. Specifically, a virtual aiming box is considered near the charging wire center, then a control design is proposed to constrain the position of the quadrotor to ensure the hooks position always within the virtual aiming box. Moreover, to enhance the system robustness against the outdoor environment, a wind disturbance estimator is proposed in the control design. Both theoretical analysis and experimental validation are carried out to demonstrate the effectiveness of the proposed strategy.

This study focuses on the multiphase flow properties of crowd motions. Stability is a crucial forewarning factor for the crowd. To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd, a novel motion structure analysis model is established based on purposiveness, and is used to describe the continuity of pedestrians’ pursuing their own goals. We represent the crowd with self-driven particles using a destination-driven analysis method. These self-driven particles are trackable feature points detected from human bodies. Then we use trajectories to calculate these self-driven particles’ purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd. Finally, we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability. Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds, and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors. Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.

Towards long range and high endurance sailing, energy is of utmost importance. Moreover, benefiting from the dominance of the sailboat itself, it is energy-saving and environment-friendly. Thus, the sailboat with energy planning problem is meaningful. However, until now, the sailboat energy optimization problem has rarely been considered. In this paper, we focus on the energy consumption optimization of an autonomous sailboat. It has been formulated as a Nonlinear Programming problem (NLP). We deal with it with a hybrid control scheme, in which pseudo-spectral (PS) optimal control method is used in heading control, and a model-free framework guided by Extreme Seeking Control (ESC) is used in sail control. The optimal path is generated with the optimal input motor torques in time series. As a result, both simulation and experiments have validated motion planning and energy planning performance. Notably, about 7% of energy is saved on average. Our proposed method can make sailboats sailing longer and sustainable.