This paper addresses the optimal state estimation problem for dynamic systems while preserving private information against an adversary. To dominate the adversary’s estimation accuracy about private information in th...
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automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the ser...
automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to,e.g.,cyber attacks,unexpected blocks of the wireless *** order to ensure AGV running safely,this paper presents a visual surveillance system by making full use of the measurements from the forward and downward ***,the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image ***,the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference *** from that,the AGV poses from the downward camera could correct scale and poses estimated the forward *** proposed method has been extensively performed on the developed *** results proves the effectiveness in using the complementary forwarddownward visual measurements for AGV security surveillance.
The ongoing improvement of advanced driving assistance systems (ADAS) is achieved due to the increasing accuracy of sensors of an ego vehicle and due to using vehicle-to-everything (V2X) communication for extra data. ...
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ISBN:
(数字)9781665404761
ISBN:
(纸本)9781665446426
The ongoing improvement of advanced driving assistance systems (ADAS) is achieved due to the increasing accuracy of sensors of an ego vehicle and due to using vehicle-to-everything (V2X) communication for extra data. In this paper, real time kinematic (RTK) technique that allows to determine high-precision relative position of ego vehicle to other vehicles as well as different infrastructure facilities is presented. The process of developing RTK sensor in autonomous driving simulator CARLA is described. The analysis of the proposed sensor approach, that is based on RTK technique is evaluated in terms of modeling the output data with normal distributed noise. The developed RTK sensor is integrated into an autonomous CARLA simulator. Finally, the simulation results are validated by practical experiment with GNSS equipment and rtklib software.
In this paper, a monocular visual-inertial odometry that utilize both point and line features is deduced. Compared with point features, line features provide more geometric information of the environment, which are mo...
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In this paper, a monocular visual-inertial odometry that utilize both point and line features is deduced. Compared with point features, line features provide more geometric information of the environment, which are more reliable in textureless scenes. However, extracting line segment features from the image are very time consuming, which will affect the real-time performance of the system. To deal with this problem, EDLines line segment detector is introduced to replace the LSD *** properties of lines are utilized to reject the mismatching of line segment feature. Pl ¨ucker coordinates and orthonormal representation of lines are used to represent 3 D lines. Afterwards, we optimize the state by minimizing a cost function consists of pre-integrated IMU residuals and visual feature re-projection residuals in a sliding window optimization framework. The proposed odometry was tested on the public datasets. The results demonstrate that the presented system can operate in real time with high accuracy.
This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need to remain private. Privacy refers to ...
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This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neu...
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ISBN:
(纸本)9781665436601
This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neural network with event-based control. We show that it can handle any unknown dynamics linear in the control input, achieving practical consensus without Zeno behaviour. A numerical example is provided to highlight the effectiveness of the proposed algorithm in terms of learning the unknown nonlinear dynamics.
Discrete abstractions have become a standard approach to assist control synthesis under complex specifications. Most techniques for the construction of a discrete abstraction for a continuous-time system require time-...
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Noise is inherited in many optimization methods such as stochastic gradient methods, zeroth-order methods and compressed gradient methods. For such methods to converge toward a global optimum, it is intuitive to use l...
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Noise is inherited in many optimization methods such as stochastic gradient methods, zeroth-order methods and compressed gradient methods. For such methods to converge toward a global optimum, it is intuitive to use large step-sizes in the initial iterations when the noise is typically small compared to the algorithm-steps, and reduce the step-sizes as the algorithm progresses. This intuition has been con-firmed in theory and practice for stochastic gradient methods, but similar results are lacking for other methods using approximate gradients. This paper shows that the diminishing step-size strategies can indeed be applied for a broad class of noisy gradient methods. Unlike previous works, our analysis framework shows that such step-size schedules enable these methods to enjoy an optimal O(1/k) rate. We exemplify our results on zeroth-order methods and stochastic compression methods. Our experiments validate fast convergence of these methods with the step decay schedules.
In this paper, we provide a generalized framework for Variational Inference-Stochastic Optimal control by using the non-extensive Tsallis divergence. By incorporating the deformed exponential function into the optimal...
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A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can ...
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A model-based offline policy iteration(PI) algorithm and a model-free online Q-learning algorithm are proposed for solving fully cooperative linear quadratic dynamic games. The PI-based adaptive Q-learning method can learn the feedback Nash equilibrium online using the state samples generated by behavior policies, without sending inquiries to the system model. Unlike the existing Q-learning methods, this novel Q-learning algorithm executes both policy evaluation and policy improvement in an adaptive *** prove the convergence of the offline PI algorithm by proving its equivalence to Newton's method while solving the game algebraic Riccati equation(GARE). Furthermore, we prove that the proposed Q-learning method will converge to the Nash equilibrium under a small learning rate if the method satisfies certain persistence of excitation conditions, which can be easily met by suitable behavior policies. Our simulation results demonstrate the good performance of the proposed online adaptive Q-learning algorithm.
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