Autonomous vehicles (AVs) are being enhanced by introducing wireless communication to improve their intelligence, reliability and efficiency. Despite all of these distinct advantages, the open wireless communication links and connectivity make the AVs’ vulnerability to cyber-attacks. This paper proposes an$L_{2}$-gain-based resilient path following control strategy for AVs under time-constrained denial-of-service (DoS) attacks and external interference. A switching-like path following control model of AVs is first built in the presence of DoS attacks, which is characterized by the lower and upper bounds of the sleeping period and active period of the DoS attacker. Then, the exponential stability and$L_{2}$-gain performance of the resulting switched system are analyzed by using a time-varying Lyapunov function method. On the basis of the obtained analysis results,$L_{2}$-gain-based resilient controllers are designed to achieve an acceptable path-following performance despite the presence of such DoS attacks. Finally, the effectiveness of the proposed$L_{2}$-gain-based resilient path following control method is confirmed by the simulation results obtained for the considered AVs model with different DoS attack parameters.

Global navigation satellite system (GNSS) signals cannot be received indoors, thus to deploy intelligent vehicles in underground car parks other localization methods are needed. In this paper, we use various carpark signs that are widely and uniformly distributed in underground parking lots as localization references. We propose a coarse-to-fine multiscale localization method that relies solely on vision sensors for underground parking lot localization based on a preconstructed lightweight node map. In coarse localization, we propose a semantic keyframe topological localization method to predict the localization range (candidate set of nodes). In node-level localization, we extract features by learning-based neural networks and construct a hybrid k-nearest neighbor (H-KNN) model to search for the closest node within the coarse localization results. In metric localization, we construct plane homography and perspective-n-point (PnP) models, allowing the vehicle’s pose (rotation and translation relative to the closest node) to be computed for refined localization. The proposed method has been tested in two underground parking lots of an office building and a shopping mall with different characteristics. Experimental results demonstrate that root mean square error (RMSE) is 0.38 m and the proposed method exhibits strong robustness in various scenarios.

At present, hydrogen fuel cell electric vehicle (HFCEV) is increasingly affordable to replace petrol vehicles and reduce carbon dioxide emissions. However, the refuelling of the HFCEV is still an essential problem. Specifically, there are not enough hydrogen refuelling stations at hand. In this paper, a hydrogen based microgrid is presented to produce hydrogen to refuel the HFCEV, and different strategies are proposed to guide the HFCEV’s refuelling within the coupled transportation network and power system. First, the HFCEV traffic flow model based on a real-world transportation network is presented. Then, a real-time simulation platform links the Sumo and Matlab is presented. Third, a hydrogen based microgrid to refuel HFCEV is built. Forth, an IEEE 30-node utility grid exporting power model is presented. At last, the real-time hydrogen refuelling of HFCEV through the coupled transportation network and power system is proposed. Four coupled structures are considered, and different HFCEV refuelling strategies (fixed price, dynamic price, LSTM decision price) are compared. The simulation results demonstrate that with the dynamic price, the congestion of the transportation network is improved, the waiting time is reduced by 17.71%, and the time loss of the network is reduced by 13.29%. With reasonable guidance of the price, vehicles choose the selected station to refuel hydrogen and influence the temporal-spatial distribution of the traffic flow of the transportation network. In addition, by adjusting the power station exporting power and the refuelling station importing power, the voltage condition of the power system can be improved.

As an emerging research field, vehicle re-identification (Re-ID) can realize identity search between the vehicles, which plays an important role in the over-the-horizon perception of Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD). At present, due to the lack of data sets, the relevant research on Vehicle-Infrastructure Cooperative (VIC) Re-ID can only be evaluated in the cross-view monitoring test set which leads to the lack of persuasion of the research. Therefore, based on the DAID-V2X dataset of Tsinghua University, this paper constructs a VIC Re-ID dataset “DAIR-V2XReid” from real vehicle scenarios through vehicle-road end target tag association, thereby making it better applicable to the research of VIC Re-ID. Owing to different task scenarios, existing algorithms trained on monitoring test sets are unable to effectively complete the Re-ID task in this new dataset. Therefore, Cross-shot Feature Aggregation Network (CFA-Net) is also proposed in this paper, to tackle the case where a vehicle becomes unrecognizable due to a large change in its visual appearance across different cameras. Firstly, we put forward a camera embedding module and add it to the Backbone, to group different cameras and solve the problem of cross-shot perspective mutation. Secondly, in order to address the situation where background and vehicle division are not distinguishable, we propose a cross-stage feature fusion module, which integrates low-order semantics with high-order semantics. Finally, we use multi-directional attention network to achieve the final feature extraction. The experimental results show that our proposed CFA-Net method achieves new state-of-the-art in DAIR-V2XReid, with mAP of 58.47%.

Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and latency. Due to poor perception of spatial features, the accuracy of the existing single-stage anchor-free detection algorithms with low latency are difficult to be implemented into autonomous vehicles. Therefore, we focus on enhancing the spatial perception ability of the anchor-free detection network based on CenterPoints. In this paper, we propose a single-stage anchor-free 3-D object detector CenterPoint-Space-Enhancement (CenterPoint-SE) algorithm and construct an efficient 3-D backbone network to extract fine-grained spatial geometric features by introducing a spatial attention mechanism and residual structure. At the same time, a powerful spatial semantic feature fusion module, the enhancement of feature fusion (EF-Fusion), is designed. In addition, we add a lightweight IoU prediction branch to improve the algorithm’s perception of various object sizes. Finally, we add a foreground point segmentation auxiliary training branch to enable the 3-D backbone to obtain object boundary features. We use the ONCE dataset to train and validate the proposed model, and the results showed that the proposed CenterPoint-SE achieves 70.33 mAP and an inference speed of 17.15 FPS, outperforming other methods.

This paper investigates the control issue of marine surface vehicles (MSVs) subject to internal and external uncertainties without velocity information. Utilizing the specific advantages of adaptive neural network and disturbance observer, a classification reconstruction idea is developed. Based on this idea, a novel adaptive neural-based state observer with disturbance observer is proposed to recover the unmeasurable velocity. Under the vector-backstepping design framework, the classification reconstruction idea and adaptive neural-based state observer are used to resolve the control design issue for MSVs. To improve the control performance, the serial-parallel estimation model is introduced to obtain a prediction error, and then a composite learning law is designed by embedding the prediction error and estimate of lumped disturbance. To reduce the mechanical wear of actuator, a dynamic event triggering protocol is established between the control law and actuator. Finally, a new dynamic event-triggered composite learning adaptive neural output feedback control solution is developed. Employing the Lyapunov stability theory, it is strictly proved that all signals in the closed-loop control system of MSVs are bounded. Simulation and comparison results validate the effectiveness of control solution.

Boresight angle misalignment is a major error source in a mobile LiDAR system (MLS), which directly affects the overall accuracy and quality of MLS scanned point clouds data. However, the current calibration of the boresight angle misalignment mainly relies on artificial target features or a manual adjustment, and the intensive labors dramatically limit the calibration flexibility. To solve these problems, this paper develops a novel on-site calibration method for boresight angle misalignment based on multiple natural features constraints, which can automatically incorporate multiple natural features extracted from surrounding environments to generate more accurate calibration results for MLS boresight angle without used any artificial targets or specific facilities. First of all, an improved 4-points congruent sets (I-4PCS) algorithm is proposed for registering the MLS point clouds in forward and backward scanned overlapping areas and realizing smooth global registration for point clouds data. Secondly, a weight principal component analysis (WPCA) approach is presented to automatically extract the appropriate multiple natural features from the well registered point clouds and establish the appropriate features representation. Thirdly, according to the extracted multiple features, the certain geometric constrains equations for spherical, linear/cylindrical, planar features are established based on a model adjustment strategy. Lastly, the boresight angle misalignment calibration can be achieved through fitting the corresponding geometric constrains equations and minimizing the weighted through a least-squares adjustment process. The experimental results demonstrate that the proposed method can effectively on-site calibrate the boresight angle misalignment error, and the overall performance of MLS is significantly improved after the calibration based on multiple natural features constraints.

Karst water may break into coal tunnels through the rock fissures and cause severe coal mine disasters. The direct current resistivity (DCR) is sensitive to underground water and can be used to detect fissures during the coal mining process. However, the three-dimensional (3D) measurement of the DCR is still a challenging task and has not been applied to the practical coal mining dynamic process. To bridge this research gap, this study proposes a new 3D cross-borehole method by monitoring the direct current resistivity at multiple points to analyze the geoelectrical field evolution in the underground coal mining process. Based on forward and inversion theoretical analysis, a DCR observation system is developed for a real unground coal mine to evaluate the cross-borehole points. The 3D resistivity distributions at different positions in the coal mining process are calculated. The analysis result demonstrates obvious resistivity changes with the evolution of the Karst water zone during the coal mining; and the location and movement of the Karst water can be well estimated. As a result, the proposed 3D cross-borehole method is very effective for monitoring the DCR and is able to accurately detect the underground water.

With the purpose of monitoring coal seam roof grouting, an innovative surface–borehole transient electromagnetic (TEM) method to compare the Earth resistivity distribution of the coal seam roof before grouting and after grouting is proposed. The TEM secondary field time–decay curve of the proposed surface–borehole TEM method is compared with that of the traditional surface TEM method. It is found that the time–decay curve of the former is very different from that of the latter. For the first time, forward modeling algorithm has been proposed to evaluate the TEM response characteristics of Earth, with an inversion algorithm developed to invert the measured data into resistivity distribution. A field work has been implemented with the customized equipment to evaluate the surface–borehole TEM method. In the field work, the proposed surface–borehole TEM measurement is performed twice, i.e., before and after the coal seam roof grouting. The analysis results demonstrated that the resistivity distribution had a distinct change near the depth of grouting with low resistivity distribution replaced by high resistivity distribution, which validates the effectiveness of the proposed surface–borehole TEM method.

Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera’s ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to simplify and reduce the size of model; d) A new far shot first-person pedestrian motion dataset under intelligent driving is specifically established to train and test the proposed model. The above four modules are integrated into the proposed deep learning model, which achieves state-of-the-art results for predicting pedestrian motion trajectory from both far and close shot first-person perspective video.

This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel’s uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel’s thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme.

The main emphasis of this work is placed on the problem of collaborative coverage path planning for unmanned surface mapping vehicles (USMVs). As a result, the collaborative coverage improved$BA^{*}$algorithm ($C C I B A^{*}$) is proposed. In the algorithm, coverage path planning for a single vehicle is achieved by task decomposition and level map updating. Then a multiple USMV collaborative behavior strategy is designed, which is composed of area division, recall and transfer, area exchange and recognizing obstacles. Moverover, multiple USMV collaborative coverage path planning can be achieved. Consequently, a high-efficiency and high-quality coverage path for USMVs can be implemented. Water area simulation results indicate that our$CCIBA^{*}$brings about a substantial increase in the performances of path length, number of turning, number of units and coverage rate.

In recent years, considerable progress has been made in semantic segmentation of images with favorable environments. However, the environmental perception of autonomous driving under adverse weather conditions is still very challenging. In particular, the low visibility at nighttime greatly affects driving safety. In this paper, we aim to explore image segmentation in low-light scenarios, thereby expanding the application range of autonomous vehicles. The segmentation algorithms for road scenes based on deep learning are highly dependent on the volume of images with pixel-level annotations. Considering the scarcity of labeled large-scale nighttime data, we performed synthetic data collection and data style transfer using images acquired in daytime based on the autonomous driving simulation platform and generative adversarial network, respectively. In addition, we also proposed a novel nighttime segmentation framework (SFNET-N) to effectively recognize objects in dark environments, aiming at the boundary blurring caused by low semantic contrast in low-illumination images. Specifically, the framework comprises a light enhancement network which introduces semantic information for the first time and a segmentation network with strong feature extraction capability. Extensive experiments with Dark Zurich-test and Nighttime Driving-test datasets show the effectiveness of our method compared with existing state-of-the art approaches, with 56.9% and 57.4% mIoU (mean of category-wise intersection-over-union) respectively. Finally, we also performed real-vehicle verification of the proposed models in road scenes of Zhenjiang city with poor lighting. The datasets are available athttps://github.com/pupu-chenyanyan/semantic-segmentation-on-nightime.

In this article, a new event-triggered$H_{\infty }$load frequency control (LFC) approach with dynamic triggered algorithm (DTA) for multi-area nonlinear power systems (NPSs) based on non-fragile proportional integral control (NPI-control) strategy is addressed. Firstly, different from the existing linear single-area LFC model for power systems, an improved nonlinear multi-area model with the performance of large-scale adjustment frequency fluctuation is constructed by considering the phenomenon of overshoots and long-term oscillations. Due to the existence of control uncertainty, it is the first time that the NPI-control scheme is applied to LFC approach for NPSs. Then, the DTA is proposed to adjust the dynamic event-triggered parameters, which reduces the occupation of communication bandwidth and the data computation of NPSs. Furthermore, a modified quadratic form with time-varying matrix and two-side closed functional method are adopted to construct the relaxed Lyapunov-Krasovskii functional, where some slack matrices are unnecessarily positive definite. Based on Lyapunov method, some less-conservatism stability criteria are derived. Utilizing the linear matrix inequality toolbox, the allowable upper bound of time-varying delays and the NPI-controller are obtained. Finally, a numerical example is presented to demonstrate the availability of the approach developed in this work.

This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the aprioriknowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations—a virtual parameter and a lower bound on the uncertain gains of the actuators—and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results.

A multimode hybrid automaton is proposed for setting vehicle platoon modes with velocity, distance, length, lane position, and other state information. Based on a vehicle platoon shift movement under different modes, decisions are made based on key conditional actions, such as sudden acceleration changes because of vehicle distance changes, emergency braking to avoid collisions and free-lane changing choices adapted to various traffic conditions, so as to ensure effortless movement and safety in the multimode shift. With a 3-degree (longitudinal, lateral, and yaw directions) of the freedom coupled model, a hybrid vehicle platoon controller is proposed using nonsingular terminal sliding-mode control to ensure fast and steady tracking on the hybrid automaton outputs during the multimode shift process. The convergence of the hybrid controller in finite time is also analyzed with the Lyapunov exponential stability. The analysis result proves that the proposed controller not only ensures the stability of the individual vehicle and the vehicle platoon but also ensures the stability of the multimode shift movement system. The proposed cooperative driving strategy for vehicle platoon is evaluated using simulations, where varying traffic conditions and the influence of cutting off are considered in conjunction with demonstration simulations of a vehicle platoon's cruising, following, lane changing, overtaking, and moving in/out of garage functions.

Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.

At present, most semantic segmentation models rely on the excellent feature extraction capabilities of a deep learning network structure. Although these models can achieve excellent performance on multiple datasets, ways of refining the target main body segmentation and overcoming the performance limitation of deep learning networks are still a research focus. We discovered a pan-class intrinsic relevance phenomenon among targets that can link the targets cross-class. This cross-class strategy is different from the latest semantic segmentation model via context where targets are divided into an intra-class and inter-class. This paper proposes a model for refining the target main body segmentation using multi-target pan-class intrinsic relevance. The main contributions of the proposed model can be summarized as follows: a) The multi-target pan-class intrinsic relevance prior knowledge establishment (RPK-Est) module builds the prior knowledge of the intrinsic relevance to lay the foundation for the following extraction of the pan-class intrinsic relevance feature. b) The multi-target pan-class intrinsic relevance feature extraction (RF-Ext) module is designed to extract the pan-class intrinsic relevance feature based on the proposed multi-target node graph and graph convolution network. c) The multi-target pan-class intrinsic relevance feature integration (RF-Int) module is proposed to integrate the intrinsic relevance features and semantic features by a generative adversarial learning strategy at the gradient level, which can make intrinsic relevance features play a role in semantic segmentation. The proposed model achieved outstanding performance in semantic segmentation testing on four authoritative datasets compared to other state-of-the-art models.

This paper addresses the co-design problem of a fault detection filter and controller for a networked-based unmanned surface vehicle (USV) system subject to communication delays, external disturbance, faults, and aperiodic denial-of-service (DoS) jamming attacks. First, an event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance. Second, an event-based switched USV control system is presented to account for the simultaneous presence of communication delays, disturbance, faults, and DoS jamming attacks. Third, by using the piecewise Lyapunov functional (PLF) approach, criteria for exponential stability analysis and co-design of a desired observer-based fault detection filter and an event-triggered controller are derived and expressed in terms of linear matrix inequalities (LMIs). Finally, the simulation results verify the effectiveness of the proposed co-design method. The results show that this method not only ensures the safe and stable operation of the USV but also reduces the amount of data transmissions.

Accurate localization is a crucial step for intelligent vehicles (IVs). And vision-based localization methods are promising due to its good accuracy and low cost. However, vision-based methods are usually not robust enough due to the errors of matching similar road scenarios. In this paper, we proposed a visual map-based localization method, called multi-view site matching (MVSM). We proposed using two camera views (i.e., downward-view and front-view) to construct visual map. The visual map consists of a serial of nodes. Each node encodes the features of the road, the 2D structure, and the poses of the vehicle. Based on the constructed visual map, we proposed a multi-scale method for accurate vehicle localization. In coarse localization, we adopt a topological model to obtain a set of candidate nodes from visual map. Furthermore, holistic features from front view are matched within the candidates such that the best matched node is determined for image-level localization. In metric localization, the best matched is first verified with the local features from downward view. And the vehicle pose is finally computed by utilizing the 2D structure from the verified nodes in the map. In the experiment, the proposed MVSM method has been tested with actual field data covering different pavement types in different seasons. The proposed MVSM method can achieve less than 0.20m mean localization errors. Compared to existing vision-based methods, the proposed method utilizes two views to enhance image-level localization and 2D pavement structure to improve metric localization so as to greatly improve the overall localization performance.

This paper aims to solve the path following problem for an underactuated unmanned-surface-vessel (USV) based on deep reinforcement learning (DRL). A smoothly-convergent DRL (SCDRL) method is proposed based on the deep Q network (DQN) and reinforcement learning. In this new method, an improved DQN structure was developed as a decision-making network to reduce the complexity of the control law for the path following of a three-degree of freedom USV model. An exploring function was proposed based on the adaptive gradient descent to extract the training knowledge for the DQN from the empirical data. In addition, a new reward function was designed to evaluate the output decisions of the DQN, and hence, to reinforce the decision-making network in controlling the USV path following. Numerical simulations were conducted to evaluate the performance of the proposed method. The analysis results demonstrate that the proposed SCDRL converges more smoothly than the traditional deep Q learning while the path following error of the SCDRL is comparable to existing methods. Thanks to good usability and generality of the proposed method for USV path following, it can be applied to practical applications.

With the aid of global position system (GPS), mobile laser scanning (MLS) is able to provide 3-D geo-referenced point cloud that has centimeter-level accuracy. The MLS accuracy, however, degrades significantly due to the trajectory errors of the laser scanner and the residual systematic errors from the geo-referencing transformation process in the GPS-free environments. To solve this problem, this article presents a novel integration algorithm based on the weighted total least squares (WTLS) and the 3-D conformal coordinate transformation (3DCCT). In this new method, the 3-D point measurement model and the error propagation parameter vector in the MLS can be updated in real-time, and they can also adjust the geo-referenced coordinate transformation parameters and eliminate the influences of the residual systematic errors during MLS. In this article, the MLS mathematical model is first established, followed up by a detailed analysis for MLS error budget interpreting the effects of the individual error sources. Second, WTLS is used to correct the 3-D point measurement model of MLS and the error of propagation parameter vector; 3DCCT, WTLS, and ground control target feature constraints are applied to eliminate the residual systematic errors in the geo-referencing transformation process. Finally, several data sets from outdoor scenarios are used to evaluate and validate the proposed method. The experimental results demonstrate that the proposed method can significantly improve the overall accuracy of the MLS system.

The accuracy of geometric parameters (mainly referred to the incidence angle and measuring distance) in a terrestrial laser scanner (TLS) is not only influenced by the TLS intrinsic systematic instrumental error but also the extrinsic received intensity data. However, the current error compensation methods for geometric parameters mainly focus on the calibration of TLS intrinsic systematic instrumental error and rarely consider the extrinsic intensity data correction. For this reason, this article presents a new method integrating the TLS intrinsic systematic instrumental error calibration and extrinsic intensity data correction to compensate the TLS geometric parameter error. The error compensation procedure is implemented as follows. First, the error compensation mathematical model integrated with TLS intrinsic systematic instrumental error calibration parameters and extrinsic intensity data correction coefficient is established. Second, the hybrid harmonic analysis (HA) and the adaptive wavelet neural network (AWNN) algorithm are proposed to calculate the TLS incidence angle error compensation values. Subsequently, the cubic spline interpolation (CSI) is applied to compute the measuring distance error compensate values. Finally, the TLS (model FARO Focus S150) and the hemispherical angle calibration instrument were used to evaluate the proposed compensation method. The experimental results demonstrate that the geometric parameters are significantly influenced by the intensity data received from TLS, and the proposed method can effectively improve the overall accuracy of the TLS incidence angle and measuring distance.

This paper presents a novel cooperative unmanned surface vehicle-unmanned aerial vehicle (USV-UAV) platform to form a powerful combination, which offers foundations for collaborative task executed by the coupled USV-UAV systems. Adjustable buoys and unique carrier deck for the USV are designed to guarantee landing safety and transportation of UAV. The deck of USV is equipped with a series of sensors, and a multiultrasonic joint dynamic positioning algorithm is introduced for resolving the positioning problem of the coupled USV-UAV systems. To fulfill effective guidance for the landing operation of UAV, we design a hierarchical landing guide point generation algorithm to obtain a sequence of guide points. By employing the above sequential guide points, high-quality paths are planned for the UAV. Cooperative dynamic positioning process of the USV-UAV systems is elucidated, and then UAV can achieve landing on the deck of USV steadily. Our cooperative USV-UAV platform is validated by simulation and water experiments.

This paper proposes a variable structure control approach for vehicles platooning based on a hierarchical fuzzy logic. The leader-follower vehicle dynamics with model uncertainties is discussed from the viewpoint of a consensus problem. A practical two-layer fuzzy control for the platooning is designed by employing two common spacing policies to ensure system robustness in different scenarios. The two policies, i.e., constant distance and constant time headway, utilize the predecessor-successor information flow from the immediate predecessor and follower other than controlled vehicles. The first layer of the fuzzy system combines spacing control with velocity-acceleration control to achieve a rapid tracking for the desired control commands, and the second layer combines the sliding mode control to adaptively compensate for reducing the state errors caused by parameter uncertainties and disturbances. Shift between different controller parameters is based on performance boundaries to guarantee the stability of individual vehicle and platooning for arbitrary initial spacing and velocity errors. These performance boundaries can be determined by using a Lyapunov method with exponential stability. Simulation of a ten-vehicle large platooning with two spacing policies shows that the control performance of the newly proposed method is effective and promising.