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Automation, Control and Robots (ICACR), International Conference on

作者： Xu Li Yili Fu Zuolun Cheng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Intelligent Robot Research Center Zhejiang Laboratory Hangzhou 311100 China

With the rapid development of human beings in motion control and localization perception, intelligent mobile robots are widely used in various scenarios, such as industrial production, medical care, logistics and distribution. Among them, wheel-legged robots have both the high mobility of wheeled robots and the high adaptability of legged robots, which can not only move fast on platform terrain, but also adapt to rugged and complex ground with the multiple degrees of freedom of their legs. Wheel-legged robots face the following main problems in high-precision robust localization in multiple scenarios: there are a lot of dynamic factors in daily indoor and outdoor scenarios, such as vehicles, pedestrians, etc., which require specific processing of dynamic environment in the visual front-end part to improve the effect of visual factor constraints; as ground mobile robots, the localization framework of visual inertial fusion (VIO) will show scale degeneration phenomenon, resulting in long-distance localization accuracy decline. In addition, as a two wheeled inverted pendulum robot, the ground constraint form will undergo corresponding changes. Aiming at the above problems, this paper takes the hydraulic-driven wheel-legged robot as the object and proposes a multi-sensor fusion localization method in dynamic scenarios.

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A Vision-Based Bird's Eye View Representation Network for 3D Objects in Open-pit Mining Area

A Vision-Based Bird's Eye View Representation Network for 3D...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Mengen Tai Wentao Liu Zhangyu Wang Bin Zhou Guizhen Yu Songyue Yang 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 Technolody Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Autonomous transportation systems, which have reconstructed open-pit mining operations, depend on accurate and real-time obstacle detection in challenging environments. Existing methods often exhibit limitations in accuracy due to their reliance on traditional image-based techniques, leading to errors and incomplete results. To address these issues, we developed a vision-based 3D object detection algorithm designed for open-pit mining environments. Our approach leverages the BEVDepth model, enabling accurate 3D object recognition using monocular camera input. Moreover, camera parameters are integrated to enhance resilience and flexibility across diverse mining environments. Last, our algorithm was implemented on a newly customized open-pit mining dataset. The experimental results verify the algorithm efficiency by attaining a high mean Average Precision score (mAP) of 67% while offering real-time performance with inference times as short as 25ms per frame. It enables accurate and efficient obstacle detection in autonomous mining vehicles and contributes to the development of safer and more productive mining operations.

关键词： Solid modeling Three-dimensional displays Accuracy Transportation Object detection Cameras Real-time systems Inference algorithms Object recognition Resilience

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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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M2I2: Learning Efficient Multi-Agent Communication via Masked state Modeling and Intention Inference

arXiv

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arXiv 2024年

作者： Sun, Chuxiong He, Peng Ji, Qirui Zang, Zehua Li, Jiangmeng Wang, Rui Wang, Wei National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences China State Key Laboratory of Intelligent Game China University of Chinese Academy of Sciences China Beijing University of Posts and Telecommunications China

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, often neglecting the critical aspect of integrating shared information. This gap can significantly impact agents' ability to understand and respond to complex, uncertain interactions, thus affecting overall communication efficiency. To address this issue, we introduce M2I2, a novel framework designed to enhance the agents' capabilities to assimilate and utilize received information effectively. M2I2 equips agents with advanced capabilities for masked state modeling and joint-action prediction, enriching their perception of environmental uncertainties and facilitating the anticipation of teammates' intentions. This approach ensures that agents are furnished with both comprehensive and relevant information, bolstering more informed and synergistic behaviors. Moreover, we propose a Dimensional Rational Network, innovatively trained via a meta-learning paradigm, to identify the importance of dimensional pieces of information, evaluating their contributions to decision-making and auxiliary tasks. Then, we implement an importance-based heuristic for selective information masking and sharing. This strategy optimizes the efficiency of masked state modeling and the rationale behind information sharing. We evaluate M2I2 across diverse multi-agent tasks, the results demonstrate its superior performance, efficiency, and generalization capabilities, over existing state-of-the-art methods in various complex scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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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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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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Driving Performance Analysis for Connected Vehicle Platoons: Velocity Perturbation and Fuel Consumption

Driving Performance Analysis for Connected Vehicle Platoons:...

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IEEE International Conference on Unmanned systems (ICUS)

作者： Yihan Zhao Xuting Duan Jianshan Zhou Long Zhang State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Zhongguancun Laboratory School of Transportation Science and Engineering Beihang University Beijing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Beijing China

Vehicle platoon is one of the concrete manifestations of multi-vehicle cooperative control and intelligent transportation. However, the velocity perturbation in the driving process will bring about stability and safety problems. The connected autonomous vehicles (CAVs) can obtain the motion status of the lead vehicle in real time and respond to it in time, which is of far-reaching significance to platoon control. Based on this, this paper focuses on the longitudinal driving strategy of connected autonomous vehicle platoons. Specifically, we provide a discrete double-integral model controller for each vehicle in a generic scenario, and the controller performs acceleration and deceleration control at each timeslot according to the state of the vehicle in front and its own state. Simulation experiments are designed by varying the size of the platoon and the number of connected autonomous vehicles. The experimental results show that the introduction of connected autonomous vehicles can effectively suppress the platoon velocity perturbation, increase the stability of the platoon driving, and significantly reduce the fuel consumption at the same time.

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A Monolithic GaN LDO Based on 12 V/0.5 μm GaN-on-Si Power Technology Achieving 20 ns Settling Time and 22 MHz UGF 16

A Monolithic GaN LDO Based on 12 V/0.5 μm GaN-on-Si Power T...

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16th IEEE International Conference on Solid-state and Integrated Circuit Technology, ICSICT 2022

作者： Wang, Peng Jiang, Yi-Zhou Liu, Dong-Sheng Zhang, You Li, Wen-Hong Huang, Wei Xiao, Zhi-Qiang Qiu, Yi-Wu Zhou, Xin-Jie Yang, Hong-Qiang Zhang, Wei Fudan University State Key Laboratory of Asic and System Shanghai Institute of Intelligent Electronics & Systems School of Microelectronics Shanghai200433 China Wuxi Microelectronics Scientific and Research Center Wuxi214035 China University of Electronic Science and Technology of China The State Key Laboratory of Electronic Thin Films and Integrated Devices Chengdu610054 China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

In this paper, a fast transient response capacitor-less gallium nitride (GaN)-based LDO on the GaN smart power technology platform for GaN integrated power regulation application is first proposed. A proposed GaN error amplifier (EA) with bootstrapping technology can effectively boost the gain to 45 dB. By employing the EA and a regulator enhancement-mode HEMT, the LDO is able to achieve a 22 MHz unit gain frequency with a load capacitor and current ranging from 0 pF to10 pF and 250 μA to 100mA, respectively. And the short settling time (20 ns) for 50-100 mA load change in 10 ns edge-time without load capacitor manifests superior high-frequency performance compared to silicon-based LDO. The circuit further promotes the development of an all-GaN solution compact power management system with improved reliability and performance. © 2022 IEEE.

关键词： Gallium nitride

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Study on Block Strategy Optimization of Passive Isolated Island Operation of DXB UHVDC Project 6th

Study on Block Strategy Optimization of Passive Isolated Isl...

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6th PURPLE MOUNTAIN FORUM on Smart Grid Protection and Control, PMF2021

作者： Huang, Run Zhang, Lifei Wu, Chen Li, Chengxiang Yang, Lei Wu, Mingkang Yunnan Power Grid Co. Ltd Kunming650011 China State Key Laboratory of HVDC China Southern Power Grid Electric Power Research Institute Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation China Southern Power Grid Electric Power Research Institute Guangzhou China

ISBN: (纸本)9789811671555

Due to the lack of generators at sending end in the early operation stage of the DXB UHVDC project, if it passively entered into island operation mode, the system will encounter harmonic instability and will cause a great risk to the steady operation. In previous projects, the common solution is to disconnect AC filters and block bipolar DC with a certain time delay when receiving island operation signal. However HVDC system may block valves, cut generation capacity or shed power by error when receiving false island operation signal. Therefore, the blocking strategy at low power under passive island mode has been improved. In DXB UHVDC project, the power on AC tie lines is used as criteria for island operation to prevent false action. The logic and realization of the control strategy are illustrated, then RTDS simulations are carried out and it is verified that by judging the power on AC tie lines, the system can effectively prevent from entering island mode by error. Finally, the field test proved that the proposed improved strategy will not cause rejecting of the existing "island blocking logic". The proposed strategy can be a reference to other DC projects. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer circuits

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The Simulation Analysis and Countermeasures of LCC-HVDC Complementary Resonance 5

The Simulation Analysis and Countermeasures of LCC-HVDC Comp...

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2023 5th International Conference on Energy systems and Electrical Power, ICESEP 2023

作者： Su, Yinsheng Liu, Zhijiang Chen, Deyang Xie, Huifan Lin, Xuehua Liu, Hongtao Power Dispatching Control Center of China Southern Power Grid Guangdong Guangzhou510663 China Electric Power Research Institute of China Southern Power Grid Guangdong Guangzhou510663 China State Key Laboratory of DC Transmission Technology Guangdong Guangzhou510663 China Key Laboratory of Guangdong New Energy Power System Intelligent Operation and Control Enterprise Guangdong Guangzhou510663 China

With the advancement of HVDC technology, the AC/DC hybrid grid has gradually emerged. The interaction and influence between AC and DC have become increasingly prominent, with the complementary resonance problem between AC and DC emerging as a hot research topic. In this paper, firstly, the phenomenon of LCC-HVDC complementary resonance is analyzed in both time and frequency domains, and a method for setting the saturation curve of the converter transformer in time domain simulation is proposed. Subsequently, targeting the actual LCC-HVDC complementary resonance, a hardware-in-loop simulation platform is constructed based on the actual HVDC control and protection equipment, and the actual field complementary resonance is reproduced. By simulating different operation modes, the operation range with a low risk of complementary resonance is determined. The influencing factors of complementary resonance are discussed and analyzed. Finally, through real-time simulation, the results show that the simulation method proposed in this article for setting the saturation curve of the converter transformer can accurately reflect the complementary resonance phenomenon between AC and DC caused by the saturation of the converter transformer. The countermeasures proposed in this article can effectively remove the complementary resonance between AC and DC and have been applied to the operation and control of actual power grids. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Time domain analysis

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