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.
This article investigates the problem of resilient dynamic event-triggered control for networked unmanned surface vehicles (USVs) with external disturbance in the presence of transmission delay and ...aperiodic DoS attacks. First, a network-based USV control system model is constructed and converted into a state space. Second, a resilient dynamic event-triggered scheme is designed to save system communication resources under aperiodic DoS attacks. Third, to ensure the security of the networked USV control system and to block communication with unknown jamming signals, a time-delay switching system model based on resilient event-triggered mechanism under aperiodic DoS attack is established. Then, by combining the Lyapunov method and linear matrix inequalities (LMIs), the criterion for asymptotic stability analysis and controller synthesis conditions of the resulting switching system is derived. Approach efficiency is validated by numerical simulation.
This article investigates the dynamic event-triggered adaptive neural coordinated disturbance rejection for marine vehicles with external disturbances as the sinusoidal superpositions with unknown ...frequencies, amplitudes and phases. The vehicle movement mathematical models are transformed into parameterized expressions with the neural networks approximating nonlinear dynamics. The parametric exogenous systems are exploited to express external disturbances, which are converted into the linear canonical models with coordinated changes. The adaptive technique together with disturbance filters realize the disturbance estimation and rejection. By using the vectorial backstepping, the dynamic event-triggered adaptive neural coordinated disturbance rejection controller is derived with the dynamic event-triggering conditions being incorporated to reduce execution frequencies of vehicle's propulsion systems. The coordinated formation control can be achieved with the closed-loop semi-global stability. The dynamic event-triggered adaptive disturbance rejection scheme achieves the disturbance estimation and cancellation without requiring the a priori marine vehicle's model dynamics. Illustrative simulations and comparisons validate the proposed scheme.
Calibration is an essential prerequisite for the combined application of light detection and ranging (LiDAR) and inertial measurement unit (IMU). However, current LiDAR-IMU calibration usually relies ...on particular artificial targets or facilities and the intensive labor greatly limits the calibration flexibility. For these reasons, this article presents a novel multifeature based on-site calibration method for LiDAR-IMU system without any artificial targets or specific facilities. This new on-site calibration combines the point/sphere, line/cylinder, and plane features from LiDAR scanned data to reduce the labor intensity. The main contribution is that a new method is developed for LiDAR extrinsic parameters on-site calibration and this method could incorporate two or more calibration models to generate more accurate calibration results. First of all, the calibration of LiDAR extrinsic parameters is performed through estimating the geometric features and solving the multifeature geometric constrained optimization problem. Then, the relationships between LiDAR and IMU intrinsic calibration parameters are determined by the coordinate transformation. Lastly, the full information maximum likelihood estimation (FIMLE) method is applied to solve the optimization of the IMU intrinsic parameters calibration. A series of experiments are conducted to evaluate the proposed method. The analysis results demonstrate that the proposed on-site calibration method can improve the performance of the LiDAR-IMU.
Over the past few years, advanced driver-assistance systems (ADASs) have become a key element in the research and development of intelligent transportation systems (ITSs) and particularly of ...intelligent vehicles. Many of these systems require accurate global localization information, which has been traditionally performed by the Global Positioning System (GPS), despite its well-known failings, particularly in urban environments. Different solutions have been attempted to bridge the gaps of GPS positioning errors, but they usually require additional expensive sensors. Vision-based algorithms have proved to be capable of tracking the position of a vehicle over long distances using only a sequence of images as input and with no prior knowledge of the environment. This paper describes a full solution to the estimation of the global position of a vehicle in a digital road map by means of visual information alone. Our solution is based on a stereo platform used to estimate the motion trajectory of the ego vehicle and a map-matching algorithm, which will correct the cumulative errors of the vision-based motion information and estimate the global position of the vehicle in a digital road map. We demonstrate our system in large-scale urban experiments reaching high accuracy in the estimation of the global position and allowing for longer GPS blackouts due to both the high accuracy of our visual odometry estimation and the correction of the cumulative error of the map-matching algorithm. Typically, challenging situations in urban environments such as nonstatic objects or illumination exceeding the dynamic range of the cameras are shown and discussed.
According to several reports published by worldwide organizations, thousands of pedestrians die in road accidents every year. Due to this fact, vehicular technologies have been evolving with the ...intent of reducing these fatalities. This evolution has not finished yet, since, for instance, the predictions of pedestrian paths could improve the current automatic emergency braking systems. For this reason, this paper proposes a method to predict future pedestrian paths, poses, and intentions up to 1 s in advance. This method is based on balanced Gaussian process dynamical models (B-GPDMs), which reduce the 3-D time-related information extracted from key points or joints placed along pedestrian bodies into low-dimensional spaces. The B-GPDM is also capable of inferring future latent positions and reconstruct their associated observations. However, learning a generic model for all kinds of pedestrian activities normally provides less accurate predictions. For this reason, the proposed method obtains multiple models of four types of activity, i.e., walking, stopping, starting, and standing, and selects the most similar model to estimate future pedestrian states. This method detects starting activities 125 ms after the gait initiation with an accuracy of 80% and recognizes stopping intentions 58.33 ms before the event with an accuracy of 70%. Concerning the path prediction, the mean error for stopping activities at a time-to-event (TTE) of 1 s is 238.01 ± 206.93 mm and, for starting actions, the mean error at a TTE of 0 s is 331.93 ± 254.73 mm.
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.
The insecticidal activity of a Ricinus communis leaf hexane extract and its fractions against adult yellow sugarcane aphids (Sipha flava) was evaluated using a contact bioassay after fumigation. The ...n-hexane extract at 10,000 ppm achieved the highest mortality (80%); the positive control had 100% mortality and the negative control had only 4% mortality over the 72-h experiment time. Chemical fractionation of the hexane extract of R. communis leaves produced multiple fractions, and 10,000 ppm of the F4 fraction resulted in 92% aphid mortality at 72 h. Gas chromatography-mass spectrometry of the F4 fraction revealed linoleic acid as the major compound (84.5%). The R. communis n-hexane extract and linoleic acid could be used for integrated pest control as an ecologically friendly alternative to synthetic chemical insecticides.
A reasonable lane work-schedule in each time period can not only guarantee the traffic efficiency of toll stations, but also reduce the operating cost of toll stations. This paper proposes a ...comprehensive solution for lane work plan. Firstly, the average queue length is selected as a good index for measuring the congestion of toll station. And then, based on the queuing theory, the service level of toll station is divided into four levels according to the relationship between the average queue length and traffic capacity. Secondly, based on the toll data, a toll station congestion prediction model is established with the Long Short-Term Memory model (LSTM) and the particle swarm optimization (PSO) algorithm. In this model, the average queue length, service time and traffic volume are selected as three inputs, the average queue length value of the next hour is the output. Thirdly, on the basis of meeting the secondary level service level of toll stations, the lane work-schedule model is established. Then, the number of lanes opened in each time period can be calculated by using this model and congestion prediction results. Fourthly, considering the two scenarios of weekday and weekend, the effectiveness of the methods proposed in this paper is analyzed and verified with the toll data of the Dongshe, Changfeng, and Linfen toll stations in Shanxi Province. Finally, based on operating costs analysis, the results show that the proposed solution could effectively realize the reasonable work-schedule of the toll station.
•A RSF map is proposed to collect the road features from multiple sensors.•A new calibration method using plane-plane correspondences is developed.•A new monocular 3D reconstruction method is ...introduced.
In order to pursue high-accuracy localization for intelligent vehicles (IVs) in semi-open scenarios, this study proposes a new map creation method based on multi-sensor fusion technique. In this new method, the road scenario fingerprint (RSF) was employed to fuse the visual features, three-dimensional (3D) data and trajectories in the multi-view and multi-sensor information fusion process. The visual features were collected in the front and downward views of the IVs; the 3D data were collected by the laser scanner and the downward camera and a homography method was proposed to reconstruct the monocular 3D data; the trajectories were computed from the 3D data in the downward view. Moreover, a new plane-corresponding calibration strategy was developed to ensure the fusion quality of sensory measurements of the camera and laser. In order to evaluate the proposed method, experimental tests were carried out in a 5 km semi-open ring route. A series of nodes were found to construct the RSF map. The experimental results demonstrate that the mean error of the nodes between the created and actual maps was 2.7 cm, the standard deviation of the nodes was 2.1 cm and the max error was 11.8 cm. The localization error of the IV was 10.8 cm. Hence, the proposed RSF map can be applied to semi-open scenarios in practice to provide a reliable basic for IV localization.
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