In industrial production, automated guided vehicles (AGVs) are widely used for material transfer and workpiece transportation to improve production efficiency. The growing demands of dynamic orders and switching produ...
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ISBN:
(数字)9789887581598
ISBN:
(纸本)9798331540845
In industrial production, automated guided vehicles (AGVs) are widely used for material transfer and workpiece transportation to improve production efficiency. The growing demands of dynamic orders and switching production bring great challenges to the dynamic scheduling problem of AGV systems. To address the dynamic tasks with different optimization objective weight factors, a self-attention based multi-objective reinforcement learning (SAMORL) AGV dynamic scheduling method is proposed in this paper. At each rescheduling point, the self-attention based multi-objective deep dueling double Q-network (SAMOD3QN) is utilized to estimate the Q-values of task allocation actions on each objective respectively. Then in the action choosing process, the Q-values on different objectives are weighted according to the given weight factors. In this way, the task allocation policy achieves quick adjustment to different optimization objectives. Furthermore, the beam search algorithm is utilized to expand the search space of the optimal action trajectory according to the cumulative reward and estimated Q-value. The effectiveness and adaptability of the proposed dynamic scheduling method is illustrated by test examples based on stochastically inserted tasks.
Effective prediction of Nitrogen Oxides(NOx) emissions in thermal power plants is essential for addressing environmental pollution control. The Selective Catalytic Reduction(SCR) system plays a pivotal role in mitigat...
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Effective prediction of Nitrogen Oxides(NOx) emissions in thermal power plants is essential for addressing environmental pollution control. The Selective Catalytic Reduction(SCR) system plays a pivotal role in mitigating NOx output, yet its performance can vary significantly due to operational conditions. This study investigates the prediction of NOx emissions in Selective Catalytic Reduction(SCR) systems within thermal power plants. Time delay mechanism is an essential factor for accurate modeling and prediction. Therefore, we incorporate a time delay of 120 samples into our feature engineering and analysis. Six predictive models, including five regression models and one Long Short-Term Memory(LSTM) neural network, are employed to analyze the temporal dynamics of NOx emissions. The regression models were selected for their varied methodologies and strengths in handling different types of data distributions and features, whereas the LSTM model was specifically utilized for its proficiency in processing sequential data, capturing long-term dependencies crucial for time-series forecasting. The performance of these models was evaluated using three statistical metrics: Root Mean Squared Error(RMSE), Mean Absolute Error(MAE), and the coefficient of determination(R2). Among the models, the Extra Trees regression model demonstrated superior accuracy in predicting NOx emissions, outperforming other models.
We investigate the problem of safe control synthesis for autonomous systems operating in environments with uncontrollable agents whose dynamics are unknown but coupled with those of the controlled system. This scenari...
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Co-design of control and communication is critical to enhancing the control performance of industrial Cyber-Physical system (ICPS). However, the stochastic features of non-deterministic communication strategies in exi...
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A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of...
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A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile ***,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random *** that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT *** algorithm simulation platform based on the Gazebo platform was *** simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more ***,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different *** experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.
Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet ***,infrastructure-based cooperative driving systems have become a means to realizing intel...
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Accurate vehicle localization is a key technology for autonomous driving tasks in indoor parking lots,such as automated valet ***,infrastructure-based cooperative driving systems have become a means to realizing intelligent *** this paper,we propose a novel and practical vehicle localization system using infrastructure-based RGB-D cameras for indoor parking *** the proposed system,we design a depth data preprocessing method with both simplicity and efficiency to reduce the computational burden resulting from a large amount of ***,the hardware synchronization for all cameras in the sensor network is not implemented owing to the disadvantage that it is extremely cumbersome and would significantly reduce the scalability of our system in mass ***,to address the problem of data distortion accompanying vehicle motion,we propose a vehicle localization method by performing template point cloud registration in distributed depth ***,a complete hardware system was built to verify the feasibility of our solution in a real-world *** in an indoor parking lot demonstrated the effectiveness and accuracy of the proposed vehicle localization system,with a maximum root mean squared error of 5 cm at 15Hz compared with the ground truth.
An approximation method that can lower the computational complexity is presented to reduce the processing time of model predictive control(MPC). The effects of the inaccurate inputs, which are caused by the approximat...
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An approximation method that can lower the computational complexity is presented to reduce the processing time of model predictive control(MPC). The effects of the inaccurate inputs, which are caused by the approximation errors, are reflected as input disturbances and are considered in the design of the controller. The closed-loop system's stability is guaranteed by a restricted Lyapunov-based constraint and input constraints, ensuring that the states will be eventually bounded at a certain confidence *** to this paper, the effects of the bounded disturbance can be observed via a change in the stability region. The relationship between the regions of the normal and approximated systems is highlighted. The proposed MPC with input disturbance and guaranteed Lyapunov stability is employed in a chemical process,and the simulation results indicate the efficiency of the proposed method.
The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismat...
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The tracking of maneuvering targets in radar networking scenarios is studied in this *** the interacting multiple model algorithm and the expected-mode augmentation algorithm,the fixed base model set leads to a mismatch between the model set and the target motion mode,which causes the reduction on tracking *** adaptive grid-expected-mode augmentation variable structure multiple model algorithm is *** adaptive grid algorithm based on the turning model is extended to the two-dimensional pattern space to realize the self-adaptation of the model ***,combining with the unscented information filtering,and by interacting the measurement information of neighboring radars and iterating information matrix with consistency strategy,a distributed target tracking algorithm based on the posterior information of the information matrix is *** the problem of filtering divergence while target is leaving radar surveillance area,a k-coverage algorithm based on particle swarm optimization is applied to plan the radar motion trajectory for achieving filtering convergence.
Policy gradient methods are one of the most successful methods for solving challenging reinforcement learning problems. However, despite their empirical successes, many SOTA policy gradient algorithms for discounted p...
The coarse pointing assembly (CPA), as the outer loop of the laser terminal, its tracking stability is the basis for ensuring laser communication. This paper presents the model of the CPA. Aiming at the disturbance fa...
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