With the continuous growth of the automobile trade, the inefficiency of traditional cargo transshipment in Roll-On/Roll-Off (RO/RO) terminals has become increasingly pronounced. As a result, the adoption of autonomous...
With the continuous growth of the automobile trade, the inefficiency of traditional cargo transshipment in Roll-On/Roll-Off (RO/RO) terminals has become increasingly pronounced. As a result, the adoption of autonomous transportation robot (ATR) for the automatic handling of finished vehicles has seen significant growth. However, ATRs designed for this purpose face several limitations, including suboptimal mobility performance and the necessity for additional infrastructure to support their operation. This paper introduces a novel ATR that offers enhanced flexibility and operational capability. To further optimize the positioning of LiDAR, we develop a multi-stage LiDAR fusion algorithm for the precise localization of finished vehicles, incorporating an event-triggered decision-making approach to improve positioning accuracy. Based on the accurate positioning data, we propose a docking strategy consisting of two key phases: the approach phase and the docking phase. During the docking phase, an enhanced Model Predictive control (MPC) algorithm, integrated with a Radial Basis Function (RBF) neural network, is designed to enable real-time adjustment of the robot’s docking attitude. The effectiveness of the proposed approach is validated through real-world robot experimentals demonstrating its practical viability.
Although different multipath error models of Delay lock loop(DLL) used in GPS receiver are established, they have never been put together for comparison. Furthermore, no universal simulation method is developed to get...
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Although different multipath error models of Delay lock loop(DLL) used in GPS receiver are established, they have never been put together for comparison. Furthermore, no universal simulation method is developed to get a fair comparison among these models. A new model with implicate expression is hence proposed for the coherent DLL and the noncoherent Dot-product(DOT) power mode DLL. Meanwhile, a new simulation method based on the anonymous function in Matlab, which is especially suitable for models with implicit expression,is also proposed to compare the new model with the existing ones. The theoretical analysis and simulation results show that the existing models are the special case of the proposed one. The new simulation method can be used for the comparison of different multipath error models and the multipath error analysis of other DLLs for which only the implicit model is available.
The optimal path planning for fixed-wing unmanned aerial vehicles(UAVs) in multi-target surveillance tasks(MTST) in the presence of wind is *** take into account the minimal turning radius of UAVs,the Dubins model is ...
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The optimal path planning for fixed-wing unmanned aerial vehicles(UAVs) in multi-target surveillance tasks(MTST) in the presence of wind is *** take into account the minimal turning radius of UAVs,the Dubins model is used to approximate the dynamics of *** on the assumption,the path planning problem of UAVs in MTST can be formulated as a Dubins traveling salesman problem(DTSP).By considering its prohibitively high computational cost,the Dubins paths under terminal heading relaxation are introduced,which leads to significant reduction of the optimization scale and difficulty of the whole ***,in view of the impact of wind on UAVs' paths,the notion of virtual target is *** application of the idea successfully converts the Dubins path planning problem from an initial configuration to a target in wind into a problem of finding the minimal root of a transcendental ***,the Dubins tour is derived by using differential evolution(DE) algorithm which employs random-key encoding technique to optimize the visiting sequence of ***,the effectiveness and efficiency of the proposed algorithm are demonstrated through computational *** results exhibit that the proposed algorithm can produce high quality solutions to the problem.
Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and ...
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Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge *** data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT *** how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a *** this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices ***,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal ***,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential ***,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis *** experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.
This study examines a multi-player pursuit-evasion game, more specifically, a three-player lifeline game in a planar environment, where a single evader is tasked with reaching a lifeline prior to capture. A decomposit...
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This study examines a multi-player pursuit-evasion game, more specifically, a three-player lifeline game in a planar environment, where a single evader is tasked with reaching a lifeline prior to capture. A decomposition method based on an explicit policy is proposed to address the game qualitatively from two main aspects:(1) the evader’s position distribution to guarantee winning the game(i.e., the escape zone),which is based on the premise of knowing the pursuers’ positions initially, and(2) evasion strategies in the escape zone. First, this study decomposes the three-player lifeline game into two two-player sub-games and obtains an analytic expression of the escape zone by constructing a barrier, which is an integration of the solutions of two sub-games. This study then explicitly partitions the escape zone into several regions and derives an evasion strategy for each region. In particular, this study provides a resultant force method for the evader to balance the active goal of reaching the lifeline and the passive goal of avoiding capture. Finally,some examples from a lifeline game involving more than one pursuer are used to verify the effectiveness and scalability of the evasion strategies.
This paper addresses the stability of networked controlsystems with aperiodic sampling and time-varying network-induced delay. The sampling intervals are assumed to vary within a known interval. The transmission dela...
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This paper investigates whether advanced neural network techniques can be applied to the detection and identification of typical targets in the context of land warfare. We collected 13 typical targets and built a dete...
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ISBN:
(纸本)9781509046584
This paper investigates whether advanced neural network techniques can be applied to the detection and identification of typical targets in the context of land warfare. We collected 13 typical targets and built a detection data set. Based on the Faster R-CNN framework, we improve the detection accuracy by two ways. First, we design a neural network model with strong local modeling capabilities. Second, we combine middle layers and the last layer of feature maps as the detection features to enhance the detection ability and improve the detection accuracy.
In this article, an optimal switching integrity attack problem is investigated to study the response of feedback controlsystems under attack. The authors model the malicious attacks on sensors as additive norm bounde...
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This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnos...
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This research provides a novel approach for detecting multi-legged robot actuator *** most significant concept is to design the Fault Diagnosis Generative Adversarial Network(FD-GAN)to fully adapt to the fault diagnosis problem with insufficient *** found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data.A straightforward solution is to use massive amounts of normal data to drive the diagnostic *** introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data.A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal *** uses a generator network as a feature extractor,and uses a discriminator network as a fault probability evaluator,which creates a new use of GAN in the field of fault *** the many learning strategies of GAN,we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the *** also design a fault location method based on binary search,which greatly improves the search efficiency and engineering value of the entire *** have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working *** compared FD-GAN with popular diagnostic *** results show that our method has the highest accuracy and recall rate.
Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algo...
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Compressive sensing is a revolutionary idea proposed recently to achieve much lower sampling rate for *** the image application with limited resources the camera data can be stored and processed in compressed *** algorithm for moving object and region detection in video using a compressive sampling is *** algorithm estimates motion information of the moving object and regions in the video from the compressive measurements of the current image and background *** algorithm does not perform inverse compressive operation to obtain the actual pixels of the current image nor the estimated *** leads to a computationally efficient method and a system compared with the existing motion estimation *** experimental results show that the sampling rate can reduce to 25% without sacrificing performance.
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