In this work,an event-triggered high gain observer(ET-HGO) design is considered for a continuous-time nonlinear system combined with disturbance and *** the event-trigger scheme,the performance of the ET-HGO depends...
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ISBN:
(纸本)9781509009107
In this work,an event-triggered high gain observer(ET-HGO) design is considered for a continuous-time nonlinear system combined with disturbance and *** the event-trigger scheme,the performance of the ET-HGO depends on the triggering condition ***,in this work,for the high-gain observer considered,an event-triggered transmission strategy,which does not relies on the other systemstate but the output of the controlsystem,is proposed such that the observation error is asymptotically ***,with mild restriction,the observation error is guaranteed to be bounded all the *** obtained theoretical results are evaluated through the numerical simulations.
As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation,localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the ob...
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As an important part of environmental perception, maps guarantee the accuracy of intelligent robots in navigation,localization and path planning. The traditional 3D maps mainly focus on the spatial structure of the objects, which lacks the semantic information. A method is proposed in the paper, this method combines convolutional neural networks(CNNs) and Simultaneous Localization and Mapping(SLAM) to create global dense 3D semantic maps for indoor scenes. The deep neural network that includes convolution and deconvolution is designed to predict semantic category of every pixel. RGB-D camera is used to obtain scene information, accomplish localization and build 3D maps simultaneously. The semantic information is integrated into the 3D scene, we present an octree map method to replace traditional point clouds method, which can reduce the error from pose estimation and single frame labeling. By this method, the accuracy of semantic information is greatly improved.
The paper presents an improved image segmentation method with a straightforward workflow for porous transducer CT images,which can be used to establish porous transducer three-dimensional model and further study its *...
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The paper presents an improved image segmentation method with a straightforward workflow for porous transducer CT images,which can be used to establish porous transducer three-dimensional model and further study its *** distribution of CT images is firstly analyzed and Gaussian filtering is conducted to reduce divergence of CT *** improved fully convolutional neural network model based on U-Net,for which multi-channel images are set as network input,is trained using training *** proposed method improves pore connectivity of the segmentation *** of porosity and permeability relative errors as well as MIOU on test set shows that the proposed method is an effective and generic two-phase segmentation method for porous transducer CT images without need of adjusting any parameters.
The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations...
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The Correlation filtering algorithm is not effective for fast deformation and fast movement. It is easy to lose when encountering problems such as occlusion. However, it has a good advantage of dealing with situations such as motion blur and lighting changes. A tracking algorithm based on color statistical features has a good effect on the rotation and translation of *** Staple algorithm combines the two algorithms to track using complementary fusion, but it also does not handle the occlusion and other issues well. In this paper, based on the Staple algorithm, the average peak correlation energy(APCE) and the maximum response are introduced. The value is used as the tracking confidence, and a detector using a support vector machine(SVM) is *** the tracking confidence is low, the target is blocked or moved violently. At this time, the detector works,and the search area is expanded around the original area for the target. At the same time, because the traditional tracking algorithm uses a fixed learning rate to update the template, this paper uses an adaptive tracking learning rate. When the tracking confidence is low, the update speed of the target model is reduced, which can effectively deal with the occlusion deformation in the tracking process. OTB100 benchmark experiments show that this method can solve the occlusion problem during target tracking. The degree of change is robust and stability.
Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products, and human forces play a pivotal role in updating the tags where the pandemic has strictly ...
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This paper proposes a new method of linear inverted pendulum control, which is used to control the quadruped robot walking on regular uneven terrain such as ramp and stair, furthermore, this method can be applied to a...
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ISBN:
(纸本)9781479973989
This paper proposes a new method of linear inverted pendulum control, which is used to control the quadruped robot walking on regular uneven terrain such as ramp and stair, furthermore, this method can be applied to a more rugged terrain. This paper analyzes the dynamic parameters of a quadruped robot walking on the complex terrain, which means the supporting points of the robot lie in different heights relative to the level ground. In order to ensure the same leg length, there will be energy dissipation caused by the raise of the center of mass. Therefore this paper proposes a dual length linear inverted pendulum method (DLLIPM), which not only effectively reduces the energy dissipation, but also promotes the workspace utilization. In addition, Newton-Raphson algorithm is used to optimize the linear inverted pendulum movement, which makes the movement symmetrical and smooth. Finally this paper presents simulation results with DLLIPM on a quadruped robot with 16 degrees of freedom.
Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a ...
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Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a target assignment model based on heterogeneous sensor networks is proposed. The characteristics of LSS targets and the influence of environmental factors such as climate and terrain are considered on this model. At the same time, the detection capabilities of different detection nodes and collaborative tracking capabilities are also considered. Secondly, an intelligent optimization algorithm GSAPSO based on PSO is proposed to maximize tracking effect of the sensors. Finally, the simulations are used to compare the GSAPSO algorithm with the traditional PSO and GAPSO algorithms. The results demonstrate that the proposed algorithm has better tracking effect.
In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagat...
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In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.
作者:
Zheng ZhiPeng ZhihongChen JieSchool of Automation
Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081 School of Automation
Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081
This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network (MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model an...
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This paper presents two aggregation strategies in convex intersection region for the distributed mobile sensor network (MSN) with heterogeneous dynamics. First, the authors analyze individual local perception model and dynamics model, set the intersection of all the local perceptions as the region of interest (ROI). The MSN consists of sensors with first-order dynamics and second-order dynamics. Then, the authors design a control strategy to ensure that individuals aggregate at a point in the ROI relying on their local perceptions and the locations of neighbors within their communication scope. The authors describe this situation of aggregation as rendezvous. In addition, the authors introduce artificial potential field to make sensors deploy dispersedly in a bounded range near the ROI, which the authors call dispersed deployment. Finally, the authors prove the stability of the proposed strategies and validate the theoretical results by simulations. This research is applied for the cooperative deployment and data collection of mobile platforms with different dynamics under the condition of inaccurate perception.
This paper investigates the problem of observer-based output feedback control for networked controlsystems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary wit...
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This paper investigates the problem of observer-based output feedback control for networked controlsystems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.
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