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Calibration of the internal and external parameters of wheeled robot mobile chasses and inertial measurement units based on nonlinear optimization

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arXiv 2020年

作者： Peng, Gang Lu, Zezao Tan, Zejie He, Dingxin Li, Xinde Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan School of Automation South East University Nanjing

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit (IMU) to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an IMU arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal reference calibration methods available for IMUs, external reference calibration methods between the IMU and the chassis of a mobile robot are lacking. This study addresses this issue by proposing a novel chassis-IMU internal and external parameter calibration algorithm based on nonlinear optimization, which is designed for robots equipped with cameras, IMUs, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the IMU and the internal and external parameters of the camera. All of the internal and external reference calibrations are conducted using the robot's existing equipment without the need for additional calibration aids. The feasibility of the method is verified by its application to a Mecanum wheel omnidirectional mobile platform as an example, as well as suitable for other type chassis of mobile robots. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile robots

Relevant Region Prediction for Crowd Counting

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arXiv 2020年

作者： Chen, Xinya Bin, Yanrui Gao, Changxin Sang, Nong Tang, Hao Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Department of Information Engineering and Computer Science University of Trento

Crowd counting is a concerned and challenging task in computer vision. Existing density map based methods excessively focus on the individuals’ localization which harms the crowd counting performance in highly congested scenes. In addition, the dependency between the regions of different density is also ignored. In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM). Each pixel in the count map represents the number of heads falling into the corresponding local area in the input image, which discards the detailed spatial information and forces the network pay more attention to counting rather than localizing individuals. Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency. The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations. Experimental results on three datasets show that our method obviously outperforms other existing state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

High Confidence Tracking with Offline Historical Learning and Online Correlation Filter Updating

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High Confidence Tracking with Offline Historical Learning an...

作者： Shou-dong HAN Hong-wei WANG Xin-xin XIA Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Research Institute of Huazhong University of Science and Technology in Shenzhen

Target tracking is one of the most challenging tasks in computer vision. In this paper, the high confidence tracking(HCT) algorithm is proposed by combining the offline historical learning network with online correlation filter updating model. First, the weighted historical targets are introduced into the offline learning network, which solves the problem of target loss caused by inaccurate tracking of the previous frame. Second, the target's confidence detection mechanism is proposed, and added to the correlation filter tracking algorithm, so that the model drift is avoided. Finally, we form a new high confidence tracking algorithm with offline learning. Compared with the state-of-the-art tracking algorithm, our algorithm performs outstandingly on benchmark OTB13 and OTB15, while ensuring real-time performance.

关键词： High confidence Target tracking Offline learning Online updating

Modelling brain based on canonical ensemble with functional MRI: A thermodynamic exploration on neural system

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arXiv 2021年

作者： Li, Wei Zhou, Chenxi Yang, Bin Fan, Wenliang Chen, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology WuhanHubei430074 China Image Processing and Intelligent Control Key Laboratory of the Education Ministry of China WuhanHubei430074 China Department of Radiology Union Hospital Tongji Medical College Huazhong University of Science and Technology WuhanHubei430074 China

Objective. Modelling is an important way to study the working mechanism of brain. While the characterization and understanding of brain are still inadequate. This study tried to build a model of brain from the perspective of thermodynamics at system level, which brought a new thinking to brain modelling. Approach. Regarding brain regions as systems, voxels as particles, and intensity of signals as energy of particles, the thermodynamic model of brain was built based on canonical ensemble theory. Two pairs of activated regions and two pairs of inactivated brain regions were selected for comparison in this study, and the analysis on thermodynamic properties based on the model proposed were performed. In addition, the thermodynamic properties were also extracted as input features for the detection of Alzheimer's disease. Main results. The experiment results verified the assumption that the brain also follows the thermodynamic laws. It demonstrated the feasibility and rationality of brain thermodynamic modelling method proposed, indicating that thermodynamic parameters could be applied to describe the state of neural system. Meanwhile, the brain thermodynamic model achieved much better accuracy in detection of Alzheimer's disease, suggesting the potential application of thermodynamic model in auxiliary diagnosis. Significance. (1) Instead of applying some thermodynamic parameters to analyze neural system, a brain model at system level was proposed from perspective of thermodynamics for the first time in this study. (2) The study discovered that the neural system also follows the laws of thermodynamics, which leads to increased internal energy, increased free energy and decreased entropy when system is activated. (3) The detection of neural disease was demonstrated to be benefit from thermodynamic model, implying the immense potential of thermodynamics in auxiliary *** Codes 80-10, 82-10, 92B99 Copyright © 2021, The Authors. All rights reserved.

关键词： Brain

Probabilistic Robust Parity Relation based Fault Detection Using Biased Minimax Probability Machine ⁎

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IFAC-PapersOnLine 2020年第2期53卷 646-651页

作者： Yujia Ma Yiming Wan Maiying Zhong Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China College of Electrical Engineering and Automation Shangdong University of Science and Technology Qingdao 266590 China

This paper proposes a probabilistic robust parity relation based approach to fault detection of stochastic linear systems. Instead of assuming exact knowledge of disturbance distribution, the uncertainty of distribution information is taken into account by considering an ambiguity set of disturbance distributions. The biased minimax probability machine scheme is exploited to formulate an integrated design of the parity vector/matrix and the detection threshold. It maximizes the worst-case fault detection rate (FDR) with respect to selected reference faults, while ensuring a predefined worst-case false alarm rate. Firstly, a scalar residual design is derived in an analytical form. The analysis of its FDR in the presence of an arbitrary fault shows its limitation due to using a single reference fault. This issue is further addressed by proposing a vector residual design with a systematic method to select multiple reference faults. The efficacy of the proposed approach is illustrated by a simulation example.

关键词： Fault detection parity relation biased minimax probability machine false alarm rate fault detection rate

A polynomial chaos approach to robust H∞ static output-feedback control with bounded truncation error

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arXiv 2021年

作者： Wan, Yiming Shen, Dongying E. Lucia, Sergio Findeisen, Rolf Braatz, Richard D. Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan430074 China Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States TU Dortmund University Dortmund44227 Germany Otto-von-Guericke University Magdeburg Magdeburg39106 Germany

This article considers the H∞ static output-feedback control for linear time-invariant uncertain systems with polynomial dependence on probabilistic time-invariant parametric uncertainties. By applying polynomial chaos theory, the control synthesis problem is solved using a high-dimensional expanded system which characterizes stochastic state uncertainty propagation. A closed-loop polynomial chaos transformation is proposed to derive the closed-loop expanded system. The approach explicitly accounts for the closed-loop dynamics and preserves the L2-induced gain, which results in smaller transformation errors compared to existing polynomial chaos transformations. The effect of using finite-degree polynomial chaos expansions is first captured by a norm-bounded linear differential inclusion, and then addressed by formulating a robust polynomial chaos based control synthesis problem. This proposed approach avoids the use of high-degree polynomial chaos expansions to alleviate the destabilizing effect of truncation errors, which significantly reduces computational complexity. In addition, some analysis is given for the condition under which the robustly stabilized expanded system implies the robust stability of the original system. A numerical example illustrates the effectiveness of the proposed approach. © 2021, CC BY.

关键词： Feedback control

Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management

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arXiv 2022年

作者： Li, Yuanzheng He, Shangyang Li, Yang Shi, Yang Zeng, Zhigang School of Artificial Intelligence and Automation Key Laboratory on Image Information Processing and Intelligent Control of Ministry of Education Huazhong University of Science and Technology Wuhan430074 China China-Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan430074 China China-EU Institute for Clean and Renewable Energy Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering Northeast Electric Power University Jilin132012 China The Department of Mechanical Engineering University of Victoria VictoriaBC V8P 5C2 Canada

The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this paper tackles this practical yet challenging issue by proposing a federated multi-agent deep reinforcement learning (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy-sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data is not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge national laboratory distributed energy control communication lab microgrid (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

Edge-Aware Graph Attention Network for Ratio of Edge-User Estimation in Mobile Networks

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Edge-Aware Graph Attention Network for Ratio of Edge-User Es...

International Conference on Pattern Recognition

作者： Jiehui Deng Sheng Wan Xiang Wang Enmei Tu Xiaolin Huang Jie Yang Chen Gong PCA Lab the Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Hong Kong Polytechnic University Hong Kong SAR China

Estimating the Ratio of Edge-Users (REU) is an important issue in mobile networks, as it helps the subsequent adjustment of loads in different cells. However, existing approaches usually determine the REU manually, which are experience-dependent and labor-intensive, and thus the estimated REU might be imprecise. Considering the inherited graph structure of mobile networks, in this paper, we utilize a graph-based deep learning method for automatic REU estimation, where the practical cells are deemed as nodes and the load switchings among them constitute edges. Concretely, Graph Attention Network (GAT) is employed as the backbone of our method due to its impressive generalizability in dealing with networked data. Nevertheless, conventional GAT cannot make full use of the information in mobile networks, since it only incorporates node features to infer the pairwise importance and conduct graph convolutions, while the edge features that are actually critical in our problem are disregarded. To accommodate this issue, we propose an Edge-Aware Graph Attention Network (EAGAT), which is able to fuse the node features and edge features for REU estimation. Extensive experimental results on two real-world mobile network datasets demonstrate the superiority of our EAGAT approach to several state-of-the-art methods.

关键词： Deep learning Fuses Estimation Switches Pattern recognition

Resilient Distributed Predefined Time Secondary control for Cyber-Physical Microgrids

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IET Renewable Power Generation 2025年第1期19卷

作者： Junfeng Tan Fan Zhang Yanlu Huang Shuai Zhao Hongyu Su China Southern Power Grid Digital Grid Research Institute Co. Ltd. China Southern Power Grid Artificial Intelligence Technology Co. Ltd. Guangzhou China School of Artificial Intelligence and Automation and Technology and also with the Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China

This paper proposed a resilient distributed predefined-time sliding mode control for islanded AC microgrids with external disturbances caused by noisy circumstances or cyber-attacks. By utilizing the predefined-time convergence theory, the voltage regulation and frequency restoration as well as active power sharing can be achieved within a predefined time, which is directly equal to an adjustable parameter. Furthermore, based on the integral sliding mode control approach, the proposed method can completely compensate the external disturbance. Different from some voltage control methods based on complex second-order consensus, a novel secondary controller is designed by adopting the virtual control technique, such that the voltage regulation can be achieved under a first-order consensus with a corresponding tracking controller. In addition, the direct Lyapunov method is utilized to prove the stability of islanded AC microgrids under the proposed controller, and the analysis of predefined-time convergence is also given. Finally, case studies on a microgrid test system with four distributed generator is built in the MATlab/SimPowerSystems software environment are conducted to demonstrate the effectiveness and superior performance of the proposed control scheme.

关键词： distributed power generation Microgrids robust control