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Aiming at solving trajectory planning problem with complex distributed no-fly zone constraints, this paper proposed a novel obstacle avoidance strategy. For longitudinal motion, an angle of attack adjustment method is employed to adjust lift and design the angle of attack profile, while adjusting the bank angle for range and altitude correction to meet terminal constraints. For lateral motion, this paper developed enhanced attractive, repulsive, and velocity potential fields. Combined with the proposed repulsive force reconstruction method, this effectively resolves the overmaneuvering problem of traditional artificial potential field methods (APFMs) for vehicle. In order to avoid mismatched magnitudes of attractive and repulsive forces, a complementary no-fly zone avoidance strategy based on minimum turn radius is introduced, updating the bank angle command during no-fly zone avoidance. Simulation results indicate that the proposed strategy can address the avoidance of sudden threat, proving to be feasible and effective for handling complex distributed no-fly zone avoidance problems. [ABSTRACT FROM AUTHOR]

Compact multi-rotor unmanned aerial vehicles (UAVs) can be operated in many challenging environmental conditions. In case the UAV requires certain considerations in designing like lightweight, efficient propulsion system and others depending upon the application, the hybrid UAV comes into play when the usual UAV types cannot be sufficient to meet the requirements. The propulsion system for the UAV was selected to be coaxial rotors because it has a high thrust-to-weight ratio and to increase the efficiency of the propulsion system, a unique propeller was proposed to achieve higher thrust. The proposed propeller was uniquely designed by analyzing various airfoil sections under different Reynolds's number using X-Foil tool to obtain the optimum airfoil section for the propellers. Since the design with duct increases efficiency, the Hybrid UAV presented in this paper has the modified novel convergent–divergent (C–D)-based duct which is a simplified model of a conventional C–D duct. The yawing and rolling maneuverings of the UAV could be achieved by the thrust vectoring method so that the design is simpler from a structural and mechanical perspective. The use of UAVs has risen in recent years, especially compact UAVs, which can be applied for applications like surveillance, detection and inspection, and monitoring in a narrow region of space. The design of the UAV is modeled in CATIA, and its further performance enactment factors are picked from advanced computational simulations relayed bottom-up approach. The predominant computational fluid dynamics (CFD) and fluid structure interaction (FSI) investigations are imposed and optimized through Computational Analyses using Ansys Workbench 17.2, which includes analysis of structural behaviour of various alloys, CFRP and GFRP based composite materials. From the structural analysis Titanium alloy came out to be the best performing materials among the others by having lower total deformation and other parameters such as normal and equivalent stress. The dynamics control response is obtained using MATLAB Simulink. The validations are carried out on the propeller using a thrust stand for CFD and on the duct through a high-jet facility for structural outcomes to meet the expected outcome. [ABSTRACT FROM AUTHOR]

The way in which public transport buses are driven has an influence in users'perception and satisfaction with the service. Bus driver's behavior is usually obtained surveying passengers and/or using the mystery passenger method, not necessarily allowing for an objective and continuous evaluation. In this work, we introduce a novel methodology to automatically classify drivers' behavior in a more consistent and objective manner, based on data from inertial measurement units, and machine learning techniques. By substituting human evaluators with automatic data collection and classification algorithms, we are able to reduce the subjectivity and cost of the current methodology, while increasing sample size. Our approach is based on three components: i) data capture using inertial measurement units (e.g. mobile devices), ii) carefully tuned classifiers that deal with sample imbalance problems, and iii) an interpretable scoring system. Results show that collected data captures several types of undesirable maneuvers, providing a rich information to the classification process. In terms of categorization performance, the evaluated classifiers, namely support vector machines, decision trees and k-NN, deliver high and consistent accuracy after the tuning process, even in the presence of a highly imbalanced sample. Finally, the proposed driver's behavior score shows high discriminative power, effectively characterizing differences between drivers, and providing driver-tailored driving recommendations, that can be generated in specific spots, in order to improve passengers' experience. The resulting methodology can be cost-effectively deployed at a large scale with good performance. [ABSTRACT FROM AUTHOR]

In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved. [ABSTRACT FROM AUTHOR]

This paper aims to reach high-quality attitude control and large-amplitude slosh suppression when a typical liquid-filled spacecraft executes three-axial large-angle maneuvers, by applying a wave-based attitude controller (WBAC). First, the sloshing dynamics is modeled by using a spherical pendulum, whose motion is expressed by splitting its coordinates. Thus, the large-amplitude lateral sloshing and the rotary sloshing as well as the over-all rigid motion of a liquid with respect to the tank in spacecraft can be approximately described. Second, the dynamics equations of system in terms of hybrid coordinate for the liquid-filled spacecraft are derived via the Lagrangian formulation. Third, an improved WBAC for three-axial large-angle maneuvers of in-orbit liquid-filled spacecraft is designed by adding a derivative control law and a gravity-gradient-torque compensation law to a wave-based control law. Simulations of three-axial large-angle maneuver demonstrate good performance of the WBAC in shortening completion time of attitude maneuvering, suppressing the jitter in the angular velocity of spacecraft, and accelerating the large-amplitude slosh suppression, simultaneously. The results indicate the potential applications of the proposed WBAC in the large-angle attitude maneuvering of liquid-filled spacecraft when there exists large-amplitude liquid slosh. [ABSTRACT FROM AUTHOR]

Slosh dynamics in a liquid propellant tank during a spacecraft maneuver can have an effect on the system stability and potentially lead to critical problems for the mission operation. In particular, the fuel sloshing problem has drawn significant attention in aerospace applications where a large portion of the gross weight is comprised of liquid fuel. Recently, many active control methods have been introduced as alternatives to passive approaches. However, the majority of these methods basically require the full-state condition. To guarantee the availability of the full-state condition, this paper proposes a cascade nonlinear state observer based on the sliding mode theory for estimating the unmeasurable slosh variables in a planar model. Additionally, for large amplitude estimation, a complete nonlinear mathematical model with a pendulum analogy is formulated without a linearization process. The stability analysis is theoretically proven by Lyapunov method. The effectiveness is demonstrated through simulations, and the performance comparison with a different observer method is also presented in the results. [Display omitted] • Observer design of cascade structure for slosh-variable estimation. • Complete nonlinear equation with pendulum analogy for slosh dynamics. • Full-state availability and large amplitude estimation are achieved. • The stability and small bound convergence are theoretically proven. • The effectiveness and performance comparison by numerical simulations. [ABSTRACT FROM AUTHOR]

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications. [ABSTRACT FROM AUTHOR]

Aeromagnetic measurement plays an important role in mineral exploration, but unmanned aerial vehicles generate maneuvering noise during aerial flight, which negatively impacts the accuracy of aeromagnetic measurement data. Therefore, aeromagnetic compensation is an indispensable step in aeromagnetic data processing. The multicollinearity of variables in the aeromagnetic compensation model based on linear regression affects its accuracy, resulting in a large difference in the compensation effect of the same group of compensation coefficients in different directions. In order to obtain high-quality aeromagnetic data, this study proposes an adaptive model-based method for suppressing aeromagnetic maneuvering noise. First, due to the fact that the variables that cause multiple collinearity in the compensation model are related to the flight heading, the model variables are adaptively assigned to each heading based on the characteristics of the variable data for different headings. The compensation model is optimized and improved, and the impact of multiple collinearity is thus suppressed. In adaptive modeling, variables with greater significance and smaller multicollinearity are automatically allocated to build the optimal heading model, and then high-precision compensation coefficients are obtained. This algorithm was applied to the data collected by a certain unmanned aerial vehicle aeromagnetic measurement platform in Ma'anshan and compared with traditional methods. The experimental results show that the adaptive modeling-based aeromagnetic compensation algorithm is superior to traditional algorithms, with fewer errors and a higher improvement ratio. Hence, the method can effectively solve the ill-conditioned problem of a model affected by multicollinearity and further improve its compensation accuracy and robustness. Moreover, the feasibility and value of this algorithm were verified in actual mineral resource detection. [ABSTRACT FROM AUTHOR]

In view of the problems of maneuvering targets tracking when the Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), such as the gradient disappears and explodes due to the long sequence, which leads to the poor tracking effect after the target maneuvers, a Transformer-Based Network (TBN) is proposed.TBN uses an encoder designed based on attention mechanism to extract the historical track features of the target sequence and improves the ability of capturing the maneuvering situation of the target.Using the Center Max(CM) normalization method, all sequences are subtracted from their initial values, which reduces the complexity of network learning and enhances the generalization of network.The results show that the position accuracy and velocity accuracy of the CM normalization and TBN method are improved by 11.2% and 41.9%, compared with the LSTM network in the large track data set with maneuvering conditions.The method proposed in this study can track the target correctly even when the observed value is missing. [ABSTRACT FROM AUTHOR]

This special issue represents some of the recent developments within argumentation studies. The following overview provides some historical context for the five papers which constitute this special issue. The Aristotelian roots of modern argumentation studies are briefly presented, as well as some further developments within Greek and Roman rhetoric. Furthermore, the most important developments in argumentation studies during the last few decades are sketched. Several modern approaches to argumentation theory are introduced, among them Perelman/Olbrechts-Tyteca's New Rhetoric, Toulmin's model of argumentation, formal dialectics, fallacy theory, informal logic, the Argumentum Model and Pragma-Dialectics. Finally, the thematic focus of the five papers is described. [ABSTRACT FROM AUTHOR]

In dealing with the different ways in which argumentative styles manifest themselves in various communicative practices from several communicative domains, van Eemeren and Garssen start from a definition of argumentative style that is connected with the pragma-dialectical notion of strategic maneuvering. Depending on the argumentative moves that are made, the dialectical routes that are followed, and the strategic considerations that are brought to bear, they make a general distinction between detached and engaged argumentative styles. In this article, they report on recent research in which it is examined to what extent the argumentative styles that are prototypically used in different institutional macro-contexts from the political, the diplomatic, the juridical, the faciliatory, the academic and the medical domain belong to these two categories. In the analyses they discuss, the authors combine specialized and domain-specific background knowledge with pragma-dialectical insight into the properties of argumentative discourse that determine the argumentative style. [ABSTRACT FROM AUTHOR]

An adaptive controller for the tracking of large-angle maneuvers is proposed for spacecraft equipped with an active pointing ultraquiet platform (AQP). The inertial parameters of the spacecraft and the payload (supported by the AQP) are estimated online, whereas the parameters of the AQP are assumed to be precisely known. To enable the derivation of the proposed controller, a novel dynamic model of AQP spacecraft was derived, which employs relative motion variables for the payload and is linear in the unknown inertial parameters. A Lyapunov-like argument was used to prove the local asymptotical stability of the closed-loop system, and the original controller was simplified further to a physically more intuitive proportional-differential (PD) plus adaptive feed-forward controller. The superior tracking performance of the proposed controller compared with that of a standard PD plus nonadaptive feed-forward controller was shown via numerical simulation. It also was shown that the estimates of a selected set of unknown inertial parameters will converge to their true values if a certain kind of maneuver trajectory is tracked. [ABSTRACT FROM AUTHOR]

Objectives: Kalman filter is frequently used in global navigation satellite system kinematic positioning applications. However, due to the irregularity of carriers’ movement, the dynamic model is often deviated and the positioning accuracy is decreased. Methods: To solve this problem, two adaptive filtering algorithms are proposed to weaken the effects of dynamic model bias based on the estimated turning rate of coordinated turn (CT) model. One is the filtering algorithm that combines CT model with an improved ellipsoid constraint equation. The other is a 3D turning model for real-time estimation of the turning rate through the analysis of the carriers’ movement. An adaptive filtering algorithm that combines 3D turning model and the adaptive factor constructed by the innovation vector is proposed. Results: The experimental results illustrate that the two algorithms can control the influence of the dynamic model errors well under different maneuvering conditions, and their accuracy is significantly better than that of standard Kalman filter and the filtering algorithm combining CT model with constant velocity model. Conclusions: In particular, the second algorithm not only improves the accuracy of dynamic model by adaptive estimation, but also further controls the disturbance influence of dynamic model by adaptive factor, which significantly enhances the accuracy and reliability of the navigation solutions. [ABSTRACT FROM AUTHOR]

For maneuvering target tracking, the interactive multiple model (IMM) algorithm and its variants have shown good performance, among which the variable structure IMM (VSIMM) algorithm is the most widely studied one. The approximation degree of the algorithm and the matching degree of the model subset will affect the performance of the VSIMM. In addition, considering that smoothing can provide better estimation of the target state, a new adaptive VSIMM filtering and smoothing (AVSIMMFS) algorithm is proposed in this paper. First, an accurate model of the VSIMM algorithm is established, in which the IMM is run in parallel with and independently from the different model subsets, and the state estimation of the model subset with the highest probability is selected as the final estimation result. Then, an adaptive VSIMM (AVSIMM) algorithm was designed based on the VSIMM algorithm. The adaptation is reflected in the construction of a new model subset from the original model subsets, which improves the matching degree between the model subset and the actual maneuvering model of the target. Finally, by smoothing the filtering data of the AVSIMM algorithm, the AVSIMMFS algorithm is obtained. Because of the combination of forward filtering and backward smoothing, the target tracking accuracy is further improved. Simulation results show that the tracking performance of the AVSIMMFS algorithm is better than that of other methods. [ABSTRACT FROM AUTHOR]

A vehicle's longitudinal acceleration is a parameter often used for determining vehicle motion dynamics. This parameter can also be used to evaluate driver behavior and passenger comfort analysis. The paper presents the results of longitudinal acceleration tests of city buses and coaches recorded during rapid acceleration and braking maneuvers. The presented test results demonstrate that longitudinal acceleration is significantly affected by road conditions and surface type. In addition, the paper presents the values of longitudinal accelerations of city buses and coaches during their regular operation. These results were obtained on the basis of registration of vehicle traffic parameters in a continuous and long-term manner. The test results showed that the maximum deceleration values recorded during the tests of city buses and coaches in real traffic conditions were much lower than the maximum deceleration values found during sudden braking maneuvers. This proves that the tested drivers in real conditions did not have to use sudden braking. The maximum positive acceleration values recorded in acceleration maneuvers were slightly higher than the acceleration values logged during the rapid acceleration tests on the track. [ABSTRACT FROM AUTHOR]

The article focuses on compares and contrasts the "original" cold war with today's "new" cold war, utilizing the lessons of the past to more deeply understand present and future conflicts. It mentions conflict between the superpowers, was marked by political struggle, espionage, and diplomatic maneuvering.. It also mentions end of the cold war also gave rise to a period of trade liberalization, leading to the creation of the North American Free Trade Agreement (NAFTA).

Orbit determination (OD) for spacecraft with unknown maneuver is always a challenging task. This paper proposes a novel framework for efficiently solving continuously maneuvering spacecraft OD problems by merging the Long Short-Term Memory (LSTM) neural network and the filter algorithms. A polynomial-representation fitted the unknown continuous maneuver is first proposed. Rather than directly output the estimated maneuver, the LSTM is trained to detect the unknown maneuver and then estimate the coefficients of the polynomial-representation. Then a fusion is designed to combine the prediction of the LSTM and the estimation of the filter for a more accurate estimation. The proposed LSTM-based framework is successfully applied to solve a Low-Earth-orbit OD problem and a Middle-Earth-orbit OD problem. The continuously maneuvering target is well tracked and the unknown maneuver is accurately estimated. Numerical simulations show that the LSTM trained based on one training dataset can also be applied to other scenarios that share some common features with the training dataset. • A framework for solving maneuvering spacecraft OD problems is proposed. • A polynomial-representation is used to fit the unknown maneuver. • A fusion strategy is designed to combine the predictions of the LSTM and estimations of the filter. [ABSTRACT FROM AUTHOR]

Marine cycloidal propeller (MCP) is well known for its maneuvering capability. It has unique kinematics. During operation, for each rotation of the propeller disc, each one of the blades undergoes one oscillation. There is phase angle between the oscillations of different blades. The face and back of the propeller blade interchange during each oscillation. These characteristics distinguish it from a typical screw propeller. In a screw propeller, the face and back of the propeller do not interchange and the blade pitch remains constant during each propeller revolution. Due to above reasons, the blades of cycloidal propellers are subjected to higher fluctuation in the loading. This causes fatigue loading on the blade and load variation on the prime mover. Therefore, during cycloidal propeller design, fatigue and load variation on the blade needs to be investigated. In this paper, structural dynamics of a cycloidal propeller blade during maneuvering conditions is investigated. The maneuvering conditions considered are bollard pull (towing), crash stop, cruising, crabbing, turning and zigzag (Planar Motion Mechanism). For analysis, the blade is considered as a thick plate. Variation of plate thickness and asymmetric support characteristics to account for aerofoil section and stock support are considered. Nonlinear finite element method is used for structural analysis. For computing the hydrodynamic load, a simple model is used. The analysis gives a good insight into the vibration characteristics of the cycloidal propeller blade. [ABSTRACT FROM AUTHOR]

The BeiDou-3 global navigation satellite system (BDS-3) has been widely used in various fields worldwide since its official launch on July 31, 2020. As of August 1, 2021, 33 satellite orbital maneuver events have occurred in BDS-3. The satellite orbits deviate owing to external factors, and orbital maneuvers are required to adjust the orbit to ensure the appropriate operation and service of the system. This study is the first systematic analysis of historical orbital maneuvers since the launch of BDS-3, eight stations that received BDS-3 signals in the Asia-Pacific region and India were selected, and 33 maneuver events of BDS-3 were detected and enumerated using the time-differenced carrier phase velocity measurement algorithm. The results revealed that the method can detect all orbital maneuvers of BDS-3, and the detection results are consistent with the maneuver dates marked by the broadcast ephemeris. In most maneuver events, the detection start and end times were approximately 10–60 min and 30–90 min earlier, respectively, than the marked time in the broadcast ephemeris. The orbital maneuvers of inclined geosynchronous orbit satellites significantly affected the velocity measurements, resulting in a mean error of approximately 0.06–0.73 m/s for each velocity component. Furthermore, the maneuvering of geostationary orbit satellites had a little effect on their velocity, and the mean velocity measurement error was mostly within 0.09 m/s. Among these satellites, the maneuvering of the C60 satellite had a smaller effect on the velocity measurement, and the mean velocity measurement error was within 0.03 m/s. The weak response at the ground level led to the weak detection sensitivity of this method to the maneuver of C60. [ABSTRACT FROM AUTHOR]

The main purpose of dredging is to maintain the required depth of harbors and marine and inland waterways. Any investment work in port infrastructure should be planned in such a way as to minimize interference with the port operations and vessel traffic. The research aims to analyze work scenarios involving a proposed dredging fleet using a Navi-Trainer Professional 5000 navigational and maneuvering simulator connected to the Transas Navi-Sailor 4000 navigation information system. An analysis performed using these electronic chart display and information system (ECDIS) simulators allows the operational parameters of the dredging project to be determined. These parameters include driving time, time taken to fill a TSHD dredger to full capacity, scale of maneuverability of the dredger within the port area, and determination of the safe distance between dredging vessels and other ships. The proposed solution may be supplemented with additional computer-based programs, providing a valuable tool in the research and monitoring of planned and current dredging projects. [ABSTRACT FROM AUTHOR]

When tracking maneuvering targets, recurrent neural networks (RNNs), especially long short-term memory (LSTM) networks, are widely applied to sequentially capture the motion states of targets from observations. However, LSTMs can only extract features of trajectories stepwise; thus, their modeling of maneuvering motion lacks globality. Meanwhile, trajectory datasets are often generated within a large, but fixed distance range. Therefore, the uncertainty of the initial position of targets increases the complexity of network training, and the fixed distance range reduces the generalization of the network to trajectories outside the dataset. In this study, we propose a transformer-based network (TBN) that consists of an encoder part (transformer layers) and a decoder part (one-dimensional convolutional layers), to track maneuvering targets. Assisted by the attention mechanism of the transformer network, the TBN can capture the long short-term dependencies of target states from a global perspective. Moreover, we propose a center–max normalization to reduce the complexity of TBN training and improve its generalization. The experimental results show that our proposed methods outperform the LSTM-based tracking network. [ABSTRACT FROM AUTHOR]

Industrial subsea infrastructure inspections using autonomous underwater vehicles (AUV) require high accuracy of AUV navigation relative to the objects being examined. In addition to traditional navigation tools with inertial navigation systems and acoustic navigation equipment, technologies with video information processing are also actively developed today. The visual odometry-based techniques can provide higher navigation accuracy for local maneuvering at short distances to objects. However, in the case of long-distance AUV movements, such techniques typically accumulate errors when calculating the AUV movement trajectory. In this regard, the present article considers a navigation technique that allows for increasing the accuracy of AUV movements in the coordinate space of the object inspected by using a virtual coordinate reference network. Another aspect of the method proposed is to minimize computational costs for AUV moving along the inspection trajectory by referencing the AUV coordinates to the object pre-calculated using the object recognition algorithm. Thus, the use of a network of virtual points for referencing the AUV to subsea objects is aimed to maintain the required accuracy of AUV coordination during a long-distance movement along the inspection trajectory, while minimizing computational costs. [ABSTRACT FROM AUTHOR]

The proposed work uses fixed lag smoothing on the interactive multiple model-integrated probabilistic data association algorithm (IMM-IPDA) to enhance its performance. This approach makes use of the advantages of the fixed lag smoothing algorithm to track the motion of a maneuvering target while it is surrounded by clutter. The suggested method provides a new mathematical foundation in terms of smoothing for mode probabilities in addition to the target trajectory state and target existence state by including the smoothing advantages. The suggested fixed lag smoothing IMM-IPDA (FLs IMM-IPDA) method's root mean square error (RMSE), true track rate (TTR), and mode probabilities are compared to those of other recent algorithms in the literature in this study. The results clearly show that the proposed algorithm outperformed the already-known methods in the literature in terms of these above parameters of interest. [ABSTRACT FROM AUTHOR]

The paper presents a new approach to the evaluation of human factor (HF) influence on the risk of maritime accidents. This approach is based on a formal human-ship-environment system definition including the relationships between humans, technology, environment, and organization. Social and organizational factors have already been included in the International Maritime Organization regulations; however, the individual factors still need investigation. A combination of psychological studies and technical operations of sea-going ships has given a huge opportunity to use HF assessment in rule-making processes. The main scientific goal of the research presented in the paper was the development of a method to assess the influence of HF on the risk of maneuvering accidents in restricted waters. This method is based on research within the area of technical ship operation and the results of the psychological profile of operators. The proposed model is based on a quantitative HF model developed by an authorized psychologist comprising personality traits, vulnerability to stress, and risk approach. The investigations were carried out with a group of 32 experienced ship masters performing a complex maneuvering task on the Full Mission Ship Handling Simulator. The multidimensional dependencies between variables of the psychological profile and the risk of an accident resulting from maneuvering errors were implemented into the Sugeno fuzzy model. The developed model allows risk assessment to be conducted that depends on the selected personality profile features. These features can be measured using psychological questionnaires, and then the risk of an accident due to maneuvering error can be calculated for a captain or marine pilot in order to improve human resource management. [ABSTRACT FROM AUTHOR]

Maneuvering-target tracking is a critical topic for target tracking. However, suffering from unknown and changeable target movements, tracking performance of common algorithms still leaves large rooms for improvement. Recently, neural network-based algorithms are proposed to greatly improve the tracking performance, while their generalization ability remains a problem. That is, when the target states distribute beyond the range of training set, their tracking errors will dramatically increase. To conquer this problem, this paper proposes a Cross-and-Dot-Product neural network based filtering algorithm. Specifically, a network is designed to estimate transition matrices with turn rate information of moving targets by calculating the cross-product and dot-product data. Thereby, our network can correctly understand the maneuvering-target movements as real-time constant turn models, instead of a database-limited end-to-end mapping. Furthermore, an adaptive probability allocation method is designed to form a double-channel filtering algorithm. The proposed algorithm derives the final tracking results by fusing the states from two unscented Kalman filters together: one filter is based on the constant velocity model, and the other is based on the model with the transition matrices estimated by network. The simulation results verify that the proposed algorithm outperforms other state-of-the-art algorithms both in tracking performance and generalization ability. [ABSTRACT FROM AUTHOR]

In this paper, a fine-tuning method of the parameters in the MMG model for the real-scale ship is proposed. In the proposed method, all of the arbitrarily indicated target parameters of the MMG model are tuned simultaneously in the framework of SI using time series data of real-sale ship maneuvering motion data to steadily improve the accuracy of the MMG model. Parameter tuning is formulated as a minimization problem of the deviation of the maneuvering motion simulated with given parameters and the real-scale ship trials, and the global solution is explored using CMA-ES. By constraining the exploration ranges to the neighborhood of the previously determined parameter values, the proposed method limits the output in a realistic range. The proposed method is applied to the tuning of 12 parameters for a container ship with five different widths of the exploration range. The results show that, in all cases, the accuracy of the maneuvering simulation is improved by applying the tuned parameters to the MMG model, and the validity of the proposed parameter fine-tuning method is confirmed. • Fine-tuning method for the parameters of the MMG model is proposed based on the framework of SI. • Parameter values determined based on captive model tests are automatically fine-tuned for real-scale ship MMG model. • Time series data of real-scale ship trials are used. • Previously determined parameter values are utilized to realize realistic tuning. • Improvement of the accuracy of the MMG model with tuned parameters is shown in a parameter fine-tuning example for a container ship. [ABSTRACT FROM AUTHOR]

This paper is concerned with multiple maneuvering target tracking problems. Although the interacting multiple model (IMM) algorithm can be used for maneuvering targets tracking, the performance is limited as the motion mode set of the target is fixed in advance. In this paper, we address the problem of multiple maneuvering targets with unknown maneuvering models in cluttered environments. A finite hidden Markov model (HMM) is developed with truncated Hierarchical Dirichlet process (HDP) to recognize the unknown and changing motion modes. Further, the HDP-HMM is introduced into multiple hypothesis tracking (MHT) and probabilistic multiple hypothesis tracking (PMHT) approaches for multiple maneuvering targets tracking with data association uncertainty. To joint estimate the HDP-HMM parameters and target states, the particle learning and variational inference approaches are used, and the solutions of HDP-HMM-MHT and HDP-HMM-PMHT algorithms are obtained based on Rao-Blackwellized particle filter and variational Bayesian methods, respectively. As the changes of maneuver models can be learned using HDP, the multi-target tracking accuracy of the proposed algorithm is improved. Simulation results show that the proposed algorithms outperform the traditional IMM-MHT method with fixed model sets. [ABSTRACT FROM AUTHOR]

Due to the constraint of minimum antenna area, 2-D ambiguity resolving is a challenging task in ship focusing of single-channel SAR mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing and positioning algorithm based on 2-D ambiguity resolving is proposed. First, we analyze the constraint of minimum antenna area and the distribution of the 2-D ambiguity area. An optimal-PRF SAR concept is proposed, where the signal ambiguity is evenly distributed to the range and azimuth directions. In this concept, the timing sequence of the orthogonal phase-coded waveform is designed to make the target echoes in different regions orthogonal. Then, an orthogonal matching filter is used to suppress the signal energy in range ambiguity regions. Aiming at the target defocus and position shift caused by the Doppler ambiguity, we propose an azimuth ambiguity resolving method. The Doppler ambiguity number can be estimated by residual envelope inclination. Subsequently, the target can be relocated and accurately focused by the estimated Doppler parameters. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results and real data processing are presented to validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical. [ABSTRACT FROM AUTHOR]

In many space missions, spacecraft are required to have the ability to avoid various obstacles and finally reach the target point. In this paper, the path planning of spacecraft attitude maneuver under boundary constraints and pointing constraints is studied. The boundary constraints and orientation constraints are constructed as finite functions of path evaluation. From the point of view of optimal time and shortest path, the constrained attitude maneuver problem is reduced to optimal time and path solving problem. To address this problem, a metaheuristic maneuver path planning method is proposed (cross-mutation grey wolf algorithm (CMGWO)). In the CMGWO method, we use angular velocity and control torque coding to model attitude maneuver, which increases the difficulty of solving the problem. In order to deal with this problem, the grey wolf algorithm is used for mutation and evolution, so as to reduce the difficulty of solving the problem and shorten the convergence time. Finally, simulation analysis is carried out under different conditions, and the feasibility and effectiveness of the method are verified by numerical simulation. [ABSTRACT FROM AUTHOR]

In inverse synthetic aperture radar (ISAR) imaging for a uniformly moving rigid-body target, a finely focused ISAR image can be obtained by using the conventional range-Doppler algorithm. However, the ISAR image quality may significantly deteriorate when the time-vary Doppler phases in virtue of target maneuvering motions are present, such as an airplane with nonuniformly rotation and a ship with fluctuation. This has become a challenging task, especially under nonhigh signal-to-noise ratio (SNR) environment. In this article, a novel ISAR imaging algorithm for a maneuvering target with moderate reflection intensity is proposed. After motion compensation, the radar echo signal in a range cell is modeled as a multicomponent cubic phase signal (CPS), in which the chirp rate and the quadratic chirp rate are two important physical quantities that may determine the target ISAR focusing quality. Based on a symmetrical instantaneous autocorrelation function, the received CPSs are transformed into the time and lag-time plane, and then a 2-D coherent integration can be realized after the generalized time-scaled transform and 1-D maximization. This forms a high-quality ISAR image. The effectiveness and superiority of the proposed algorithm are validated by the ISAR imaging results of simulated and real measured data. [ABSTRACT FROM AUTHOR]

• Experimental and numerical methods are applied to estimate the turning ability of ships with and without incident waves. • The model ships considered are with and without bulbous bow. • The numerical methods can predict the overall turning tendency well. • It shows that ship speeds and wavelength provide critical effects on turning characteristics. • Different manoeuvrability is observed for the ships with and without bulbous bow. The present study introduces an experimental and numerical investigation of the turning ability of two ships with and without a bulbous bow: original and modified KVLCC2 hulls Experiments are performed via free-running model tests in a square basin, and a system-based simulation are performed. The maneuvering coefficients for the system-based solver are obtained via numerical planar motion mechanism (PMM) tests based on CFD computations, and other variables are obtained from reference ships. The effects of ship speed, wave direction, and wavelength are investigated. Results of a calm-water turning test obtained experimentally and numerically agree well. When the ship turns in the presence of incident waves, the overall behavior and evaluation parameters predicted in the simulation are similar to those obtained from experiments. However, the evaluation parameters show scattered values under severe turning-failure conditions. The most significant wave effects are observed in short waves. Additionally, the turning ability is compared between two ships, and slightly different maneuvering capabilities are observed. The time series for two hulls are further compared to investigate the reason for the difference showing the difference in the force and drift angle time series. [ABSTRACT FROM AUTHOR]

We derive optimal control policies for a Connected Automated Vehicle (CAV) and cooperating neighboring CAVs to carry out a lane change maneuver consisting of a longitudinal phase where the CAV properly positions itself relative to the cooperating neighbors and a lateral phase where it safely changes lanes. In contrast to prior work on this problem, where the CAV "selfishly" only seeks to minimize its maneuver time, we seek to ensure that the fast-lane traffic flow is minimally disrupted (through a properly defined metric). Additionally, when performing lane-changing maneuvers, we optimally select the cooperating vehicles from a set of feasible neighboring vehicles and experimentally show that the highway throughput is improved compared to the baseline case of human-driven vehicles changing lanes with no cooperation. When feasible solutions do not exist for a given maximal allowable disruption, we include a time relaxation method trading off a longer maneuver time with reduced disruption. Our analysis is also extended to multiple sequential maneuvers. Simulation results show the effectiveness of our controllers in terms of safety guarantees and up to 16% and 90% average throughput and maneuver time improvement respectively when compared to maneuvers with no vehicle cooperation. [ABSTRACT FROM AUTHOR]

The advancement of artificial intelligence has enabled UUVs to provide asymmetrical capabilities in various domains, particularly in the preparation of the battle space. With the development of unmanned aerial vehicle (UAV) technology, fiber-optic wire-guided UUV-launched UAV may provide the real-time communication including target information, communication relay and command issuance for UUVs. When the UAV produces relative motion between itself and the submerged UUV, the length and configuration of the fiber cable change greatly, the near-field environment is complex, and there is a risk of winding the hull. Additionally, the fiber cable is fragile and cannot withstand the excessive bending deformation due to the communication principle, so it is crucial to investigate the configuration changes of underwater fiber cables during UAV and UUV maneuvering. In this paper, based on the lumped mass method, the numerical calculation model of variable length cable is established, and the numerical solver is compiled to simulate the optic cable of UAV underwater navigation and UUV maneuvering. The dynamic simulation of cable variable length release is realized, and the influence of various dynamic parameters of UAV and UUV on the configuration of variable length cable is analyzed. • Based on the lumped mass method, the numerical calculation model of variable length cable is established. • The motion response of the fiber cable during the motion between UAV and UUV is obtained and analyzed. • The influence of various dynamic parameters on the configuration of variable length cable is obtained. [ABSTRACT FROM AUTHOR]

Ship collision avoidance is critical to maritime safety, but maritime traffic patterns are becoming increasingly complex as the number of vessels of all types increases. Therefore, how to improve the collision avoidance effectiveness and efficiency of two ships and multiple ships is crucial. This study proposes a comprehensive collaborative collision avoidance framework that takes into account both safety and economic factors. By analyzing ship navigation characteristics, we formulate a new collision avoidance cost function that covers navigation safety and economic benefits. Furthermore, in accordance with the COLREGS guidelines, this paper adopts a deterministic search algorithm to calculate collision avoidance trajectories involving two or more vessels, and uses a proportional integral differential (PID) algorithm to adjust ship maneuvering based on the derived collision avoidance decisions. In parallel, we conducted extensive simulation studies to evaluate encounter scenarios involving two and multiple vessels. Results show that the proposed collaborative collision avoidance framework ensures regulatory compliance and also improves navigation safety and economic efficiency. This algorithm has important reference value for ship operators to formulate and implement effective collision avoidance strategies. • We focus on the crucial need for multi-ship collision avoidance in the context of growing number of ships in complex waters. • A novel deterministic search algorithm is designed for two-ship and multi-ship cooperative collision avoidance. • An innovative collision avoidance cost function is designed for distributed multi-ship collision avoidance that integrates safety and economic advantages, improving decision-making in collision avoidance scenarios. • The COLREGS is aligned in the method, ensuring the proposed collision avoidance strategies are in accordance with international maritime regulations. [ABSTRACT FROM AUTHOR]

Path planning is a key factor for the unmanned surface vehicle (USV) to achieve efficient navigation. In this paper, to solve the global path planning and obstacle avoidance problems for the USV, an improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed. Three main improvement parts are composed of the proposed algorithm. Firstly, the radial basis function (RBF) neural network is combined with the Q-Learning algorithm to approximate the action value function Q, which improves the convergence speed of the Q-Learning algorithm. Secondly, to ensure that the planned path conforms to the maneuvering characteristics of the USV, the heading angle, motion characteristics, ship length, and safety of the USV are taken into account by the proposed algorithm. Based on these factors, the action space and reward function are optimized, the state space is reconstructed, and the safety threshold is proposed. Finally, a third-order Bezier curve is used to smooth the initial path, so that the USV can maintain its heading stability during navigation. Based on simulation results, the proposed NSFQ algorithm outperforms the A* and RRT algorithms in terms of evaluation indicators such as heading angle, angular velocity, path length, sailing time, and path smoothness. • An improved Q-Learning algorithm called neural network smoothing and fast Q-Learning (NSFQ) is proposed in this paper. • The heading angle, angular velocity, and other indicators of the USV are taken into account by this proposed algorithm. • A safety threshold is proposed to ensure the safety of the USV. • A third-order Bezier curve is used to smooth the initial path. [ABSTRACT FROM AUTHOR]

Recently, Hybrid MIMO phased-array (H-MIMO-PA) radar systems have been demonstrated to offer enhanced target tracking capabilities. In practice, an effective radar resource allocation (RA) strategy can ensure the quality of service (QoS) of subtasks and improve the system resource utilization in multiple target tracking (MTT) applications. In this paper, a robust power allocation strategy is developed with the objectives of enhancing target tracking accuracy (TTA) and low probability of intercept (LPI) performance in H-MIMO-PA radar. Firstly, in order to accurately grasp the air defense battlefield situation and improve the QoS for MTT, we propose a threat assessment model based on the intuitionistic fuzzy entropy (IFE) to calculate the comprehensive threat degree of each target in real time. Next, since the predicted conditional Cramér-Rao lower bound (PC-CRLB) is based on the most recently realized measurement and provides an accurate lower bound, the PC-CRLB in clutter is derived and utilized to formulate the task utility function for TTA. In addition, the probability of intercept is derived and adopted as the performance metric for LPI. Due to power allocation process may become conflicting for achieving better TTA and LPI performance at the same time, the Pareto-based bi-objective optimization scheme is adopted, which formulates a non-convex optimization problem. Finally, to solve the formulated model, we propose the modified bi-objective particle swarm optimization (MBOPSO) algorithm. Simulation results are provided to verify the effectiveness of the proposed power allocation algorithm. [ABSTRACT FROM AUTHOR]

Current control laws for active control of helicopter structural vibration are designed for steady-state flight conditions, while the vibration response of maneuvering flight has not been taken into consideration yet. In order to obtain full-time vibration suppression capability, the authors propose a filtered least mean square-mixed sensitivity robust control method based on reference signal reconstruction (LMS-MSRC), driving piezoelectric stack actuators to suppress helicopter structural vibration response in maneuvering flight. When feedback controller designed by H ∞ theory is implemented, active damping is added on the secondary path to weaken the adverse effects of its sudden changes in maneuvering flight state. Furthermore, a reference signal reconstruction scheme is given concerning equivalent secondary path. In addition, the reconstruction accuracy, the convergence speed, stability, and global validity of the hybrid controller are analysed. Compared with multichannel Fx-LMS, numerical simulations of LMS-MSRC for vibration suppression are undertaken with a helicopter simplified finite element model under several typical flight conditions. Further experiments of real-time free-free beam vibration control are performed, driven by a stacked piezoelectric actuator. The instantaneous overshoot of measured response is 42% less than the peak value and its attenuation reaches 85% within 2.5 s. Numerical and experimental results reveal that the proposed algorithm is practical for suppressing transient disturbance and multifrequency helicopter vibration response during maneuvering flight with faster convergence speed and better robustness. [ABSTRACT FROM AUTHOR]

The service areas of the BeiDou Navigation Satellite System (BDS) are China, the Asia–Pacific, and the world via three stages of development. The BDS constellation is designed to maintain a geostationary Earth orbit (GEO), inclined geosynchronous orbit (IGSO), and medium Earth orbit (MEO). These orbital maneuvers yield certain difficulties for data processing, especially for the BeiDou satellites, such as a decrease in the real-time service performance, which causes a missing precise orbit product from the maneuvered satellites. The frequencies of the orbital maneuvers for the GEO and IGSO satellites are higher than those for the MEO satellites. The maneuvering time and strategy cannot be obtained by common users due to secrecy, which can yield a decline in the service performance of the BDS. Thus, in this study, we analyzed the variation in the orbital semimajor axis for the satellites. The long-term variation in the orbital semimajor axis changes linearly. The short-term variation in the orbital semimajor axis also has periodicity, which can be described by the sum of sine functions. According to the long-term variation in the orbital semimajor axis for the satellites, we propose a detection method for in-plane orbital maneuvering of the GEO, IGSO, and MEO satellites. We then propose a detection method for out-of-plane maneuvering of the GEO satellites. BDS and Global Positioning System (GPS) data from the broadcast ephemeris were analyzed to verify the proposed methods. The experimental results from 2013 to 2019 show that the frequency of orbital maneuvering is approximately once a month for the GEO satellites, once every six months for the IGSO satellites, and once every 1.4 years for the MEO satellites. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASSs) are attracting increasing attention in recent years as it brings new opportunities for water transportation. Previous studies aim to propose fully autonomous system on collision avoidance decisions and operations, either focus on supporting conflict detection or providing with collision avoidance decisions. However, the human-machine cooperation is essential in practice at the first stage of automation. An optimized collision avoidance decision-making system is proposed in this paper, which involves risk appetite (RA) as the orientation. The RA oriented collision avoidance decision-making system (RA-CADMS) is developed based on human-machine interaction during ship collision avoidance, while being consistent with the International Regulations for Preventing Collisions at Sea (COLREGS) and Ordinary Practice of Seamen (OPS). It facilitates automatic collision avoidance and safeguards the MASS remote control. Moreover, the proposed RA-CADMS are used in several encounter situations to demonstrate the preference. The results show that the RA-CADMS is capable of providing accurate collision avoidance decisions, while ensuring efficiency of MASS maneuvering under different RA. [ABSTRACT FROM AUTHOR]

This paper introduces a novel trajectory generation and optimization algorithm (TGO) that enables agile and aggressive flight of quadrotor UAVs while considering various constraints associated with robot dynamics, actuator inputs, and flight environment. The TGO algorithm employs time-parametrized polynomial trajectories based on a predetermined sequence of waypoints to produce dynamically feasible and collision-free trajectories. This approach extends previous work that utilized differential flatness property and polynomial based trajectories by eliminating the need for iterative searching and computationally intensive sampling. One of the significant advantages of the TGO algorithm is its numerical stability for large number of waypoints and high-order polynomials. To address the ill-conditioned problem of Quadratic Programming (QP) based methods, the TGO algorithm reformulates the trajectory generation and optimization problem into an unconstrained quadratic programming (UCP) using the numerically stable null-space factorization method. The TGO algorithm produces minimum derivative trajectories and minimum waypoints arrival times, generating a wide range of aggressive trajectories that can leverage the full maneuvering capabilities of quadrotor robots. The proposed algorithm's numerical stability and computational advantages are demonstrated through various scenarios and comparisons. An animated simulation of the TGO algorithm is available at: https://youtu.be/MvvhBG14iIg. • The TGO algorithm enables quadrotor UAVs to fly with aggressive and agile movements. • The TGO algorithm takes into account the dynamics of the quadrotor, the flying environment, and actuator limitations. • The TGO algorithm eliminates the need for intensive computation and searching. • The TGO algorithm can also be used to effectively solve the Unconstrained Quadratic Programming problems. • The algorithm enhances numerical stability for trajectories with a large number of waypoints and high-order polynomials. [ABSTRACT FROM AUTHOR]

• Complete model of ship propulsion system developed to study ship fuel consumption. • Engine operating & weather conditions are combined and considered in whole voyage. • Engine management modes are compared to explore a way for saving more fuel. Aiming at reducing ship emissions and lower operational cost, less fuel consumption is always pursued by shipping industry. Compared with other methods on reducing the fuel consumption, such as optimizing the design of hull lines or ship propulsion system, setting additional energy saving device, the voyage optimization is a way which is easier to implement and needs less investment cost. In this paper, an integrated simulation model of ship propulsion and maneuvering system is established and verified. Then, a benchmark 38800 t cargo ship and a 32n miles coastal service are selected for the case study. Based on the ship operating characteristic and voyage environments, 24 representative cases of combination about the voyage set up and ship control modes are proposed. The ship fuel consumptions under 24 cases are calculated using the integrated simulation model. Finally, fuel consumption change under different conditions are analyzed and the most reasonable case of saving fuel consumption is provided. This study tries to investigate the influence of multiple factors on the ship fuel consumption considering the realistic and representative operating conditions, and provides theoretical suggestions for ship propulsion and maneuvering control, voyage planning and engine management for reducing fuel consumption over ship voyages. [ABSTRACT FROM AUTHOR]

The continuous tracking of infrared dim-small targets is significant, due to limited spatial resolution and low thermal features. Tracking algorithms based correlation filter may perform not well referring to infrared information. Therefore, a Deep Learning (DL) model is proposed for the tracking task with public data sets of small targets. To be specific, the Siamese Region Proposal Network (SiamRPN) is improved by the style recalibration module, which can obtain the perception of image styles. Furthermore, the proposed algorithm takes advantage of transfer learning technology referring to labeled target images, obtaining good features. To distinguish the small target from the background edges, the side window filtering is combined with the improved SiamRPN model. The experimental results show the good performance of the proposed small target tracking, namely SiamIST, in public near-infrared videos, compared to several related algorithms. Importantly, the designed algorithm uses the DL model to track small infrared targets for the first time, achieving an overall precision of 78.8%. [Display omitted] • A deep learning-based algorithm is proposed for the first time to track a small maneuvering target of the Unmanned Aerial Vehicle (UAV) in NIR videos. • An improved SiamRPN model utilizing SRM-PF is designed to effectively complete small target tracking. • The SWF is adopted to suppress the bad influence of the edge of the image, helping to obtain a good response for small targets. • Transfer learning is employed on a public data set of the IR small target. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making algorithm for ship collision avoidance. First, an algorithm for the planar distribution of collision risk is described. The level of collision risk was calculated as a function of the plane coordinates based on the predicted encounter state. It enables the visualization of all the potential collision risks as a collision risk map and the calculation of the integrative collision risk against several ships, considering COLREGs. Second, an optimal route-planning algorithm that can consider the maneuvering characteristics of the ship is proposed. Because a ship cannot change its course and speed suddenly, the nodes and their connections can be set to limit the course and speed change amounts to appropriate ranges. The optimal route was obtained as the minimum-cost route along node connections. Finally, a decision-making algorithm for ship collision avoidance was developed by combining the proposed collision risk mapping and optimal route-planning algorithm. It was applied to representative encounter situations and evaluated by an evaluation method. Further, the proposed algorithm was compared with state-of-art algorithms. Thus, safe and efficient decision-making for collision avoidance can be achieved even in congested situations by appropriately changing both a course and a speed depending on the encounter situation. • An algorithm to calculate a planar distribution of collision risk considering COLREGs is proposed. • An algorithm for optimal routing for collision avoidance by both course and speed changes is proposed. • A decision-making method for collision avoidance is developed by combining the proposed two algorithms. • The developed method can provide a safer and more efficient collision avoidance plan even in congested situations. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making algorithm for ship collision avoidance. First, an algorithm for the planar distribution of collision risk is described. The level of collision risk was calculated as a function of the plane coordinates based on the predicted encounter state. It enables the visualization of all the potential collision risks as a collision risk map and the calculation of the integrative collision risk against several ships, considering COLREGs. Second, an optimal route-planning algorithm that can consider the maneuvering characteristics of the ship is proposed. Because a ship cannot change its course and speed suddenly, the nodes and their connections can be set to limit the course and speed change amounts to appropriate ranges. The optimal route was obtained as the minimum-cost route along node connections. Finally, a decision-making algorithm for ship collision avoidance was developed by combining the proposed collision risk mapping and optimal route-planning algorithm. It was applied to representative encounter situations and evaluated by an evaluation method. Further, the proposed algorithm was compared with state-of-art algorithms. Thus, safe and efficient decision-making for collision avoidance can be achieved even in congested situations by appropriately changing both a course and a speed depending on the encounter situation. • An algorithm to calculate a planar distribution of collision risk considering COLREGs is proposed. • An algorithm for optimal routing for collision avoidance by both course and speed changes is proposed. • A decision-making method for collision avoidance is developed by combining the proposed two algorithms. • The developed method can provide a safer and more efficient collision avoidance plan even in congested situations. [ABSTRACT FROM AUTHOR]

• Long short-term memory can be applied to predict risks related to driving on sharp curves. • The effects of road alignment and driving characteristics on driving risks can be considered using the dynamic and spatial classification of curves. • Model prediction performance is affected by observation and delay windows. • Model performance can be improved by combining the data for vehicle kinematics, driver maneuvering/behavior, driver physiological characteristics, and road alignment characteristics. Traffic accidents are likely to occur on sharp curves under poor driving conditions, and the severity level of such accidents is high. Therefore, predicting the risk associated with driving on curved roadways in real time can effectively improve driving safety. This paper aims to develop a dynamic real-time method that fuses multiple data sources to predict risk when driving on sharp curves in the context of the connected vehicle environment. Six curves with three small radii (60 m, 100 m, 150 m) and two driving directions (left and right) were designed for a driving simulation experiment. Driver maneuvering data, vehicle kinematic data, and physiological data of 55 drivers were collected for this study. The data were combined and spatially and dynamically segmented. The mean value of the critical lateral acceleration of the vehicle was set as the risk assessment index. K-means clustering was used to classify the driving risk into three levels: low, medium, and high. Then, the risk level was predicted using the maneuvering data, vehicle kinematic data, and physiological data as well as road alignment characteristics as input features for the proposed model that employs the long short-term memory (LSTM) network algorithm. Models with different combinations of observation window (lookback) and interval window (delay) were compared to derive the best window combination. The algorithms selected for comparison against the LSTM algorithm are random forest, XGBoost, and LightGBM. The results show that the proposed LSTM-based method can effectively predict dangerous driving behavior on sharp curves. The optimal window combination derived using the LSTM algorithm is lookback = 20 m and delay = 20 m. The prediction performance of the proposed model is significantly better than that of the other three compared algorithms, with F1-scores of 84.8% and 86.0% for the medium and high risk categories, respectively. In addition, the proposed LSTM-based model that fuses multiple data sources is proven to outperform the model that uses only vehicle kinematics data. The dynamic prediction method proposed in this paper can contribute to the development of a real-time prediction and warning system for driving risks at vehicle terminals in the intelligent connected vehicle environment. [ABSTRACT FROM AUTHOR]

Asteroid impacts pose a major threat to all life on Earth. The age of the dinosaurs was abruptly ended by a 10-km-diameter asteroid. Currently, a nuclear device is the only means of deflecting large Potentially Hazardous Asteroids (PHAs) away from an Earth-impacting trajectory. The Enhanced Kinetic Impactor (EKI) concept is proposed to deflect large PHAs via maneuvering space rocks. First, an unmanned spacecraft is launched to rendezvous with an intermediate Near-Earth Asteroid (NEA). Then, more than one hundred tons of rocks are collected from the NEA as the EKI. The NEA can also be captured as the EKI if the NEA is very small. Finally, the EKI is maneuvered to impact the PHA at a high speed, resulting in a significant deflection of the PHA. For example, to deflect Apophis, as much as 200 t of rocks could be collected from a NEA as the EKI based on existing engineering capabilities. The EKI can produce a velocity increment (∆v) of 39.81 mm/s in Apophis, thereby increasing the minimum geocentric distance during the close encounter in 2029 by 1,866.93 km. This mission can be completed in 3.96 years with a propellant cost of 2.98 t. Compared with a classic kinetic impactor, the deflection distance can be increased one order of magnitude. The EKI concept breaks through the limitation of the ground-based launch capability, which can significantly increase the mass of the impactor. We anticipate that our research will be a starting point for efficient planetary defense against large PHAs. [ABSTRACT FROM AUTHOR]