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A novel algorithm based on nonlinear optimization for parameters calibration of wheeled robot mobile chasses

APPLIED MATHEMATICAL MODELLING 2021年 95卷 396-408页

作者： Peng, Gang Lu, Zezao Tan, Zejie He, Dingxin Li, Xinde Minist Educ Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China SouthEast Univ Sch Automat Nanjing Peoples R China

Mobile robot positioning, mapping, and navigation systems generally employ an inertial measurement unit to obtain the acceleration and angular velocity of the robot. However, errors in the internal and external parameters of an inertial measurement unit arising from defective calibration directly affect the accuracy of robot positioning and pose estimation. While this issue has been addressed by the mature internal parameters calibration method available for inertial measurement unit, external reference calibration method between the inertial measurement unit and the chassis of a mobile robot are lacking. This study ad-dresses this issue by proposing a novel algorithm for internal parameter calibration of mecanum wheel omnidirectional mobile platform and external parameter calibration of mecanum chassis-inertial measurement unit based on principal component analysis and nonlinear optimization, which is designed for robots equipped with cameras, inertial mea-surement unit, mecanum wheels, and wheel speed odometers, and functions under the premise of accurate calibrations for the internal parameters of the inertial measurement unit and the internal and external parameters of the camera. All of the internal and ex-ternal parameters 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 ap-plication to a mecanum wheel omnidirectional mobile platform. The proposed calibration method is thereby demonstrated to guarantee the accuracy of robot pose estimation. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Parameters calibration Inertial measurement unit Mobile robot chassis Nonlinear optimization

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GNet: 3D Object Detection from Point Cloud with Geometry-Aware Network

GNet: 3D Object Detection from Point Cloud with Geometry-Awa...

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4th IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Ruan, Hao Xu, Bingrong Gao, Junbin Liu, Lianguang Lv, Junting Sheng, Yin Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan Peoples R China

ISBN: (纸本)9781665490283

3D object detection based on point clouds in real-world applications has been a challenging task because the raw point cloud may be sparse or occluded. It may be caused by bad weather or limited sensor resolution, leading to decreasing detection performance. To this end, a novel high-performance Geometry-aware Network (GNet) is proposed and used to accurately detect 3D objects from sparse point clouds. Different from previous work that only regresses the offset of 3D bounding boxes numerically between ground truth (GT) and prediction, GNet considers the geometric information of 3D bounding boxes and combines the two effectively to achieve a better regression performance. In particular, Point Nets blocks learned voxel-wise features and the proposals are generated by the 3D voxel convolution neural network (voxel CNN). By utilizing the prior knowledge and the geometric properties of the 3D bounding boxes, the proposed novel OS-loss can improve detection performance and accelerate speed of inference. Furthermore, GNet takes advantage of prior geometry properties rather than only regressing the offset numerically. These speed up the convergence of the regression network. Finally, our proposed GNet was evaluated on the KITTI dataset for 3D objects and Bird's Eye View (BEV) detection aspects. The results demonstrate that GNet can improve object detection performance for sparse point clouds.

关键词： Geometry

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Synaptic Learning With Augmented Spikes

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第3期33卷 1134-1146页

作者： Yu, Qiang Song, Shiming Ma, Chenxiang Pan, Linqiang Tan, Kay Chen Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Cognit Comp & Applicat Tianjin 300050 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Inst Artificial Intelligence Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.

关键词： Neurons Task analysis Computational modeling Encoding Feature extraction Biological system modeling Biological information theory Augmented spikes neuromorphic computing pattern recognition spike encoding spiking neural networks (SNNs) synaptic learning

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Projective Synchronization Analysis of Fractional-Order Neural Networks With Mixed Time Delays

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6798-6808页

作者： Liu, Peng Kong, Minxue Zeng, Zhigang Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Peoples R China Zhengzhou Univ Light Ind Henan Key Lab Informat Based Elect Appliances Zhengzhou 450002 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

In this article, we analyze the projective synchronization of fractional-order neural networks with mixed time delays. By introducing an extended Halanay inequality that is applicable for the case of fractional differential equations with arbitrary initial time and multiple types of delays, sufficient criteria are deduced for ensuring the projective synchronization of fractional-order neural networks with both discrete time-varying delays and distributed delays. Furthermore, sufficient criteria are presented for ensuring the projective synchronization in the Mittag-Leffler sense if there is no delay in fractional-order neural networks. The results derived herein include complete synchronization, anti-synchronization, and stabilization of fractional-order neural networks as particular cases. Moreover, the testable criteria in this article are a meaningful extension of projective synchronization of neural networks with mixed time delays from integer-order to fractional-order ones. A numerical simulation with four cases is provided to verify the validity of the obtained results.

关键词： Synchronization Delays Delay effects Biological neural networks Differential equations image processing Fractional order mixed time delays neural network projective synchronization

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Pool-based unsupervised active learning for regression using iterative representativeness-diversity maximization (iRDM)

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PATTERN RECOGNITION LETTERS 2021年 142卷 11-19页

作者： Liu, Ziang Jiang, Xue Luo, Hanbin Fang, Weili Liu, Jiajing Wu, Dongrui Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Civil Engn & Mech Wuhan Peoples R China

Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 60 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Active learning Regression Unsupervised learning

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Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication

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NEURAL NETWORKS 2023年第1期167卷 168-182页

作者： Wei, Fei Chen, Guici Zeng, Zhigang Gunasekaran, Nallappan Xihua Univ Sch Sci Chengdu 610039 Peoples R China Wuhan Univ Sci & Technol Hubei Prov Key Lab Syst Sci Met Proc Wuhan 430065 Peoples R China Wuhan Univ Sci & Technol Sch Sci Wuhan 430065 Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Toyota Technol Inst Computat Intelligence Lab Nagoya 4688511 Japan Beibu Gulf Univ Eastern Michigan Joint Coll Engn Qinzhou 535011 Peoples R China

This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Finite/fixed-time synchronization Delayed inertial memristive neural networks (DIMNNs) Unified control framework Settling time functions image encryption

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A statistical distribution recalibration method of soft labels to improve domain adaptation for cross-location and cross-machine fault diagnosis

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MEASUREMENT 2021年 182卷 109754-109754页

作者： Zhang, Qing Tang, Lv Sun, Menglin Xuan, Jianping Shi, Tielin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Unsupervised domain adaptation has achieved certain success in recent cross-domain fault diagnosis research. As a widely used transfer strategy, the distribution alignment often occurs with the problems of too few valid alignment samples, too low confidence of predicted labels, and the inadequate alignment of marginal or conditional distributions. Therefore, this paper proposes a statistical distribution recalibration method of soft labels (SDRS). First, SDRS defines the valid samples and confusion interval in the statistical distribution of per-class predicted probabilities. Then, from the perspective of binary classification, a recalibration space in the confusion interval is further optimized by a center distance metric, to improve predicted confidence and valid distribution alignment. Built on SDRS, a novel cross-domain fault diagnosis approach named SDRSDAN is constructed, where dynamic distribution adaptation is used to match and adjust the marginal and conditional distribution discrepancies adaptively. Extensive experiments prove the effectiveness of SDRS-DAN in cross-location and cross-machine scenarios.

关键词： intelligent fault diagnosis Cross-location tasks Cross-machine tasks Statistical distribution recalibration Distribution alignment Dynamic distribution adaptation

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Memristive Circuit Design of Brain-Inspired Emotional Evolution Based on Theories of Internal Regulation and External Stimulation

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021年第6期15卷 1380-1392页

作者： Wang, Zilu Wang, Xiaoping Zeng, Zhigang Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

In this work, a bionic memristive circuit with functions of emotional evolution is proposed by mimicking the emotional circuit in limbic system, which can perform unconscious and conscious emotional evolutions by using theories of internal regulation and external stimulation respectively. Two kinds of memristive models, volatile and non-volatile, play key roles in the process of emotional evolution. That is, the internal regulation is mainly responsible for simulating the unconscious evolution process over time by using the forgetting effect of the volatile memristor. The external stimulation is mainly responsible for using the memristance plasticity of the non-volatile memristor to simulate the evolutionary learning behavior under the action of multi-modal inputs (such as visual, speech and text signals), so as to realize the conscious emotional evolution. A two-dimensional (2D) emotional state space consisted of valence and arousal signals is adopted, the evolution behaviors are performed on the basis of valence and arousal signals in the space, in order to achieve continuous emotional evolution and express the evolved emotions intuitively. Due to the use of memristors, the proposed circuit can realize in-memory computing, which fundamentally avoids the problem of storage wall and constructs a brain-inspired information processing architecture. The simulation results in PSPICE show that a nonlinear mapping relationship between inputs and outputs is constructed through the proposed circuit, which can carry out diversified emotional evolution based on the designed internal regulation and external stimulation evolution circuits.

关键词： Integrated circuit modeling Brain modeling Memristors External stimuli Biological system modeling Circuit implementation emotional evolution internal regulation and external stimulation memristor two-dimensional emotional space

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DAFormer: Depth-aware 3D Object Detection Guided by Camera Model via Transformers

DAFormer: Depth-aware 3D Object Detection Guided by Camera M...

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4th IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Gao, Junbin Ruan, Hao Xu, Bingrong Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Educ Minist China Key Lab Image Proc & Intelligent Control Beijing Peoples R China Wuhan Univ Technol Sch Automat Wuhan Peoples R China

ISBN: (纸本)9781665490283

3D object detection plays a critical role in autonomous driving perception. Although multi-view-based perception solutions have made significant progress, their performance is still far from being ready for practical use. The estimation of pixel depth is dependent on camera intrinsic properties, which led us to explore a depth-aware model guided by camera parameters. Our contribution in this paper is the DAFormer, which incorporates camera parameters and position-aware image features to detect 3D objects. The depth-aware module uses camera parameters to reweight image features and estimate depth, enhancing and offsetting the 3D position embedding. The object query uses depth information enhanced features for end-to-end 3D detection by Attention layers. DAFormer achieves impressive results on the standard nuScenes dataset without any additional embellishments.

关键词： Object detection

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Robust Platoon control of Industrial Heavy-Load Autonomous Vehicles 12

Robust Platoon Control of Industrial Heavy-Load Autonomous V...

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12th International Conference on CYBER Technology in Automation, control, and intelligent Systems, CYBER 2022

作者： Liu, Zhe Li, Xiang Wang, Hesheng MoE Key Lab of Artificial Intelligence Ai Institute Shanghai Jiao Tong University China School of Mechanical Engineering and Electronic Information China University of Geosciences China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Platoon control of autonomous industrial vehicles contributes to improving the safety and reliability of cargo transportation in complex industrial scenarios, increasing traffic efficiency and saving energy. The platoon control of autonomous heavy-load industrial vehicles is much harder, as the high robustness requirements, low hardware ability and complex un-structural environments. This paper considers the platoon control task from the view-point of practical industrial applications. Firstly, the longitudinal dynamic model of the heavy-load industrial vehicle is transferred into a first-order damping acceleration response model by introducing the low-level feedback linearization control and compensations. Secondly, the synchronization control concept is introduced and a fully distributed control law is presented, which only requires the coupling information of the two nearest neighbors. In particular, in order to ensure the practical applicability, the information processing delays, data samplings and external control disturbances are explicitly modelled and the global stability analysis approach is presented. Finally, we provide real experiments using full-scale industrial heavy-load vehicles to demonstrate our effectiveness and robustness. © 2022 IEEE.

关键词： Accident prevention

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