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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Jiajun Xia, Weiguo Li, Fuqiang Wang, Peng China National Aeronautical Radio Electronic Research Institute National Key Laboratory of Science and Technology on Avionics System Integration China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education School of Control Science and Engineering China

ISBN: (纸本)9781665465335

This paper studies the task assignment problem of a multi-robot system with complex constraints including the robot speed limit, the task execution time window constraints, the arrival time, the trip cost, and different priorities of the tasks. A new bidding function is then designed for each robot. Based on the k-means algorithm, a hierarchy-based extended consensus-based bundle algorithm (CBBA) is proposed to find the optimal solution for the task allocation problem. Finally, simulation examples are presented to show the feasibility of the proposed algorithm and comparisons with existing algorithms show that the proposed algorithm can reduce the task assignment time with guaranteed reward values. © 2022 IEEE.

关键词： Multipurpose robots

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Anomaly Detection and Fault Diagnosis Method for Autonomous Transport Vehicles on Unstructured Roads

Anomaly Detection and Fault Diagnosis Method for Autonomous ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Yifang Zhang Guizhen Yu Han Li Chaoqi Zhang Lecong Li Chuanying Zhang State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University School of Transportation Science and Engineering Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Autonomous vehicles in mining areas undertake substantial production tasks and are prone to various faults during operation. Early detection of abnormalities, along with timely fault warnings and diagnoses, can enhance transportation safety and increase vehicle turnout rates. This study utilizes driving data from autonomous vehicles in mining areas and considers the characteristics of unstructured road scenes. The driving area is segmented into distinct intervals, and Kullback-Leibler (KL) divergence is applied within each interval to detect anomalies in the vehicle's lateral deviation during operation. Experimental results demonstrate that the proposed method achieves an anomaly detection accuracy of 91.4%, with a false negative rate of 8.3% and a false positive rate of 8.7%.

关键词： Fault diagnosis Accuracy Roads Transportation Production Safety Logic Anomaly detection Autonomous vehicles Standards

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Cable Segmentation Based on Mask2Former in Open-Pit Mining Area

Cable Segmentation Based on Mask2Former in Open-Pit Mining A...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Liyun Wang Bin Zhou Songyue Yang Wentao Liu Huazhi Li Shengdi Sun The Key Laboratory of Autonomous Transportation Technology for Special Vehicles School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications.

关键词： Image segmentation Accuracy Computational modeling Transportation Pressing Network architecture Real-time systems Safety Optimization Cables

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Investigations of Sodium Droplet Spreading and Evaporation on Surfaces with Different Wettability Based on Lattice Boltzmann Simulations

SSRN

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SSRN 2024年

作者： Tang, Songsheng Guo, Kailun Tian, Zhixing Wang, Mingjun Wang, Chenglong He, Xiaoqiang Su, Guanghui Qiu, Suizheng Tian, Wenxi School of Nuclear Science and Technology State Key Lab. of Multiphase Flow in Power Engineering Shaanxi Key Lab. of Advanced Nuclear Energy and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Key Lab. of Nuclear Reactor System Design Technology China Nuclear Power Research and Design Institute Chengdu610213 China

The spreading and evaporation of sodium droplets is one of the fundamental phenomena in high-temperature heat pipes. The LBM sodium two-phase flow and phase transition model, which can simulate a huge liquid-vapor density ratio, is first established and validated. Then, the spreading and evaporation processes of sodium droplets on solid walls with different wettability are numerically simulated and analyzed. The main conclusions obtained are as follows: (1) The dimensionless spreading diameter and time of the droplet spreading process conforms to Tanner's power law, and the power exponent has exponential relationship with the equilibrium wetting angle;(2) The maximum dimensionless spreading diameter is negatively and linearly correlated with the equilibrium wetting angle;(3) Evaporation of sodium droplets under constant heat flow conditions shows a mixed pattern. The droplets will be detached from the droplet wall at the late stage of evaporation, and the larger the equilibrium wetting angle the sooner the droplets detach. © 2024, The Authors. All rights reserved.

关键词： Evaporation

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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Xiangyu Zhang Guoyan Xu Han Li Peng Chen Qi Xia Han Cai State Key Lab of Intelligent Transportation System School of Transportation Science and Engineering The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET -MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET -MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET -MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles.

关键词： Training Event detection Computational modeling Heuristic algorithms Predictive models Prediction algorithms Vehicle dynamics Long short term memory Frequency control Predictive control

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Real-Time 3D Object Detection Based on Dynamic Sparse Voxel Transformer in Mining Area

Real-Time 3D Object Detection Based on Dynamic Sparse Voxel ...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Shengdi Sun Zhangyu Wang Runsen Liu Songyue Yang Liyun Wang Wentao Liu State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements.

关键词： Point cloud compression Three-dimensional displays Runtime Heuristic algorithms Transportation Object detection Transformers Real-time systems Safety Data mining

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Prediction of Driving Departure of Mining Autonomous Transport Vehicles Based on GRU Network

Prediction of Driving Departure of Mining Autonomous Transpo...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Lecong Li Guizhen Yu Han Li Qi Xia Xiangyu Zhang Han Cai School of Transportation Science and Engineering State Key Lab of Intelligent Transportation System The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Open-pit mining areas have special geological structures, complex road networks, multi-rotation sections and poor road conditions, which bring many challenges to the operation of mining autonomous transport vehicles. Due to the large size and high control difficulty of mining autonomous transport vehicles, poor control effect and inaccurate steering mechanism implementation are prone to occur during the driving process, which leads to the vehicle departure from the reference path. To ensure the safety of autonomous operation in intelligent mine, this paper proposes a method for predicting driving departure of mining autonomous vehicles based on Gated Recurrent Unit (GRU). Firstly, the vehicle's historical trajectory data is obtained via on-board sensors, followed by data cleaning and normalization processes. key features are extracted, and a vehicle driving scene recognition module based on a GRU-based network is designed using deep learning. Subsequently, a GA-seq2seqGRU trajectory prediction module is constructed utilizing the scene recognition results, and the Genetic Algorithms (GA) is employed to tune the network hyper-parameters. Based on the predicted trajectories, the overall departure risk is calculated using two departure judgment methods based on cross-lane time and predicted lateral deviation, which are mapped to the departure level. Simulation experiments demonstrate that the accuracy of the driving scene recognition model in the proposed method in this paper reaches 0.9721, which is better than the 0.9585 of the Long Short Memory Neural Network (LSTM) model, and the trajectory prediction model based on the results of the driving scene recognition has a smaller RMSE value than the LSTM, GRU, and GA-seq2seqLSTM models when the prediction time domains are 1s, 2s, 3s, 4s, and 5s, and the departure detection module The average time consumed is 0.142ms.

关键词： Accuracy Roads Predictive models Feature extraction Trajectory Safety Data mining Long short term memory Autonomous vehicles Genetic algorithms

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AWNet: Negative obstacle semantic segmentation Network based on adaptive weight loss 22

AWNet: Negative obstacle semantic segmentation Network based...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhao, Jiarui Yu, Guizhen Wang, Jie Wang, Zhangyu Zhang, Junjie Feng, Yunsong School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Beihang University Beijing China Ministry of Industry and Information Technology Beihang University Beijing China Research Institute for Frontier Science Beihang University Hefei China Hefei innovation research institute of Beihang university Beihang University Hefei China State Key Laboratory of Pulsed Power Laser Technology National University of Defense Technology Heufei China

ISBN: (纸本)9798331527471

On roads, both vehicles and pedestrians will leave traces after passing by. Slight traces will not cause any trouble to the passing vehicles and pedestrians subsequently. However, there are certain potholes in the road that have been formed by repeated crushing of vehicles. It may cause mechanical damage to following vehicles and lead to accidents as vehicles get stuck in the potholes. Thus, the segmentation of negative obstacles is crucial for the safe driving of self-driving vehicles. This paper proposed an AWNet neural network to this end, and quantitative analyses were conducted based on the publicly availab.e dataset DRNO. Specifically, a random cut-and-joint module (RCJ) was proposed to augment the dataset. The cross-entropy loss function was modified by adding an adaptive weight module, i.e., the adaptive weight cross-entropy loss function. It allows the model to focus on negative obstacles during implementation. Furthermore, this paper proposed a post-processing module for adaptive viewpoint conversion based on depth maps, which is capable of fusing depth maps and RGB images to convert the front view into a BEV viewpoint. The mAcc for Negative obstacle semantic segmentation can reach 78.39%, the mIoU can reach 73.21%, the mF1 can reach 79.23%. The experimental results reveal that the AWNet is able to state-of-the-art performance compared with other networks. © 2024 IEEE.

关键词： Semantic Segmentation

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Design Knowledge Perception and Representation Method for User Requirements 6

Design Knowledge Perception and Representation Method for Us...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Fan, Guoliang Yu, Xiaoyi Gao, Yaqiong Lu, Xiaoping Jiang, Zuhua Shanghai Jiao Tong University Shanghai Key Lab of Advanced Manufacturing Environment SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University State Key Laboratory of Massive Personalized Customization System and Technology SJTUSME-COSMOPlat Joint Research Center for New Generation Industrial Intelligent Technology School of Mechanical Engineering Qingdao266100 China

ISBN: (纸本)9798350323535

The user's requirements for product design include descriptiveness, dynamics, and personalization. The insufficient mapping of User requirement knowledge seriously affects the accuracy of designers' grasp of design ideas and design knowledge. For this issue, we proposed a design knowledge perception and representation method for user requirements. Design knowledge from user requirement question and answer (Q&A) was extracted, including design theme, design content, and design evaluation to improve communication efficiency between designers and users. A representation method was proposed for User requirement knowledge based on the Q&A of the user's initiating requirements and the product provider's solutions. A design requirement framework for expressing design intent was proposed by perceiving dialogue context. Furthermore, the rule of design requirement was derived based on attributes in User requirement knowledge for designers. Finally, the knowledge perception, representation method, and knowledge framework of air conditioners were analyzed to determine the knowledge rules. The results indicated that the knowledge perception and representation method proposed in this study accurately reflected User requirement knowledge and provided sufficient knowledge for product design. At the same time, the extracted design requirement was shared among different decision-makers, providing a theoretical basis for group decision-making. © 2023 IEEE.

关键词： Knowledge representation

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Forming Circle Formations of Multi-agent systems Under Actuator Faults 5th

Forming Circle Formations of Multi-agent Systems Under Actu...

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5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021

作者： Liu, Jiajun Ru, Xinfeng Xia, Weiguo Wang, Chen China National Aeronautical Radio Electronic Research Institute and National Key Laboratory of Science and Technology on Avionics System Integration Shanghai China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian China National Engineering Research Center of Software Engineering and the State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

ISBN: (纸本)9789811939976

A distributed fault-tolerant formation control law is designed in this paper for multi-agent systems under external disturbances and actuator faults including time-varying loss of effectiveness faults. The initial position of each agent is already on a circle and the control purpose is to form a desired formation on this circle. Based on this local information, a distributed adaptive formation control law is designed by employing the congelation of variables method to deal with time-varying actuator faults. Finally, a simulation example is given to verify the effectiveness of the adaptive fault-tolerant control design method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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