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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Li, Ruitong Zhang, Tao Xu, Xiaoyu Zhao, Yuenan Song, Ran Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China

ISBN: (纸本)9781665481090

Efficient and accurate object detection from 3D point clouds is crucial for autonomous driving. Due to the sparsity and irregularity of real-world point clouds, as well as the real-time requirements for detectors, existing 3D object detectors face significant challenges in practical applications. PointPillars is widely used in real-world scene due to its fast detection speed, but its accuracy lags behind some existing methods. To enhance the detection accuracy of this model, we propose P2CBAM, which integrates CBAM into the PointPillars baseline model. This integration improves detection accuracy with relatively minimal additional computational resources. Extensive experiments conducted on the KITTI and Waymo Open datasets demonstrate that P2CBAM outperforms the baseline model on both datasets. The code is available at https://***/Bennyliiiiii/P2CBAM. © 2024 IEEE.

关键词： HTTP

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Learning to Navigate Sequential Environments: A Continual Learning Benchmark for Multi-modal Navigation

Learning to Navigate Sequential Environments: A Continual Le...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Xie, Yinduo Zhao, Yuenan Zhang, Qian Zhang, Lin Li, Teng Zhang, Wei School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China

ISBN: (纸本)9781665481090

Existing works for robot navigation typically focus on performance in specific tasks, overlooking the adaptability of navigation strategies to sequential environments. In this paper, we present CLBMN, a continual learning (CL) benchmark for multi-modal robot navigation, which aims to enhance robots' navigation capabilities in handling sequential tasks. The benchmark is composed of a multi-modal dataset, a CL-based algorithm, and three comprehensive evaluation metrics for CL-based multi-modal navigation. Specifically, we construct a multi-modal dataset with 10,000 samples by collecting images, point clouds, and odometry data from five scenarios, including daytime, nighttime, rainy conditions, highway, and field. Then, we propose an end-to-end navigation framework with momentum update to mitigate catastrophic forgetting across various environments, which provides a baseline approach for CL-based robot navigation. Furthermore, we define three evaluation metrics specifically designed to evaluate the robot's continual navigation performance across different scenarios. Experimental results demonstrate that the proposed benchmark is effective for studying multi-modal continual robot navigation in sequential environments. Our project page is available at https://***/clbmn/. © 2024 IEEE.

关键词： Robot learning

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Movement control and Attitude Adjustment of Climbing Robot on Flexible Surfaces

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第3期65卷 2618-2628页

作者： Wu, Xinyu Liu, Jia Zhou, Yimin Lv, Qin Hu, Chaofang CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems and Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Electrical and Information Engineering Tianjin University Tianjin China

In this paper, a series of control algorithms have been designed for the climbing cloth robot, "Clothbot" to climb vertically. The developed algorithm consists of three parts: a sliding mode control with integral action (SMC with integral action), a linear quadratic regulator, and dead reckoning for semi-autonomous navigation. First, the SMC with integral action is developed for high-precision position tracking and speed tracking of the permanent magnet direct current (PMDC) motor installed on the robot. The error between the expected velocity and the actual velocity of the PMDC motor is adopted to design the sliding surface. In order to compensate the fluctuation of the load torque due to the varied friction during climbing, an observer is formulated to estimate the load torque in real-time. Second, the LQR is employed for attitude adjustment by an active torque so as to prevent the robot from falling or being jammed during the movement. Third, due to the onboard-limited energy, the robot is just equippedwith semi-autonomous navigation named dead reckoning carrying on primary localization and primary path planning. Experiments are performed to verify the efficacy of the proposed control algorithms.

关键词： Climbing robot flexible surface linear quadratic regulator load observer sliding mode control with integral action

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Associations of occupation categories with cardiovascular diseases and all-cause mortality: an analysis of NHANES 2005-2014

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BMC public health 2025年第1期25卷 1847页

作者： Haiyang Chi Jia Zhou Chao Li Yuhuan Lu Can Xie Baoyu He Wei Ke School of Applied Sciences Macao Polytechnic University Macao 999078 China. Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University Macao 999078 China. Chongqing Yubei Maternal and Child Health Hospital Chongqing 40120 China. Guang Ming District Center for Disease Control and Prevention Shenzhen Guangdong Province 518106 China. The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science University of Macau Macao 999078 China. School of Applied Sciences Macao Polytechnic University Macao 999078 China. wke@mpu.edu.mo. Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education Macao Polytechnic University Macao 999078 China. wke@mpu.edu.mo.

BACKGROUND:Evidence on the effects of occupation categories on cardiovascular disease (CVD) progression and longevity has mostly come from cross-sectional studies, which limits our understanding of the pathogenesis of CVD. This study aimed to evaluate the associations of occupation categories with CVD and all-cause mortality in the American population. METHODS:We analyzed data from the 2005-2014 U.S. National Health and Nutrition Examination Survey (NHANES), a large-scale public health survey representative of the U.S. population, linked to mortality data obtained in 2019. To evaluate the association between occupation categories and the risks of cardiovascular disease (CVD) and all-cause mortality, we estimated Cox proportional hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS:Among 11,102 participants (aged 20-85 years, 53.99% male), 132 died from CVD among the total 478 deaths during a median follow-up of 9.9 years. In multivariable-adjusted models, the occupation categories were significantly associated with CVD mortality, with the hazard ratios (HRs) and 95% confidence intervals (95% CIs) of 3.95(1.94-8.04) for construction and clean-up industry, 2.51(1.15-5.52) for sales and service industry, 2.49(1.04-5.95) for business and management, and 2.98(1.56,5.71) for others. For all-cause mortality, only construction and clean-up industry and sales and service industry were positively associated with all-cause mortality, and HRs (95% CIs) were 2.05(1.33-3.16) and 1.64(1.12-2.41). Both working hours in the previous week and the number of months worked exhibited varying degrees of dose-response relationships with CVD and all-cause mortality. CONCLUSIONS:Occupation categories were found to be significantly associated with the risk of CVD and all-cause mortality. Future research could incorporate different work properties into specific prevention strategies for these outcomes.

关键词： All-cause mortality Cardiovascular mortality Cohort study Occupation categories

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A Feature-Aided Multiple Model Algorithm for Maneuvering Target Tracking

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 566-568页

作者： Yiwei Tian Meiqin Liu Senlin Zhang Ronghao Zheng Shanling Dong College of Electrical Engineering Zhejiang University National Key Laboratory of Industrial Control Technology Zhejiang University IEEE National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University

Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aide... 详细信息

关键词： underwater Aided letter

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Hierarchical Reinforcement Learning for Autonomous Parking Based on Kinematic Constraints

Hierarchical Reinforcement Learning for Autonomous Parking B...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Xu, Xiaoyu Xie, Yinduo Li, Ruitong Zhao, Yuenan Song, Ran Zhang, Wei School of Control Science and Engineering Shandong University Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China

ISBN: (纸本)9781665481090

In recent years, the rapid increase in urban vehicles has made finding vacant parking spaces a significant challenge. Our research focuses on reverse parking maneuvers that align with driver habits and enhance safety. While end-to-end Reinforcement Learning (RL) has shown promise in autonomous driving, it struggles with parking tasks requiring flexible switching between forward and reverse actions due to large exploration space and sparse rewards. To address these challenges, this paper proposes an autonomous parking algorithm based on Hierarchical Reinforcement Learning (HRL), which decomposes complex tasks into subtasks for better exploration and accuracy. The high-level controller has two functions: building a RL-based agent that maps Bird's-eye view (BEV) images and external measurements to the ego vehicle's actions, and using the Autonomous Vehicles (AVs) chassis data to establish dynamic constraint equations for calculating the motion constraint-based parking area. The low-level controller executes specific parking maneuvers. Experimental results show that compared to baseline methods, our method achieves the highest parking success rate in both static and dynamic scenarios. Additionally, our method demonstrates smoother performance during reverse parking. © 2024 IEEE.

关键词： Reinforcement learning

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An Improved Levenberg-Marquardt Algorithm with Adaptive Learning Rate for RBF Neural Network 35

An Improved Levenberg-Marquardt Algorithm with Adaptive Lear...

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第35届中国控制会议

作者： AN Ru LI Wen Jing Han Hong Gui QIAO Jun Fei College of Electronic and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781509009107

In this paper,an improved Levenberg-Marquardt(LM) algorithm with adaptive learning rate is proposed to optimize the learning process of RBF neural ***,an improved LM algorithm is adopted using a quasi-Hessian matrix and gradient vector which are computed *** with the conventional LM algorithm,Jacobian matrix multiplication and storage are not required in the improved LM algorithm,which can reduce computation cost and solve the problem of memory ***,the adaptive learning rate is integrated into the improved LM algorithm in order to accelerate the convergence speed of training algorithm and improve the network performance of nonlinear system ***,several experiments are conducted and the results show that the proposed method has faster convergence speed and better prediction performance.

关键词： Improved LM algorithm RBF neural network adaptive learning rate fast convergence speed

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Design of a Self-organizing Recurrent RBF Neural Network Based on Spiking Mechanism 35

Design of a Self-organizing Recurrent RBF Neural Network Bas...

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第35届中国控制会议

作者： LU Chao HAN Honggui QIAO Junfei YANG Cuili College of Electronic and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781509009107

Based on the systemic investigation of recurrent neural network,a self-organizing recurrent radial basis function(SR-RBF) neural network which based on the spiking mechanism and improved Levenberg-Marquardt(LM) algorithm is proposed in this *** hidden neuron in the recurrent radial basis function(RRBF) can be added or pruned by computing the spiking strength of the connections between hidden and output neurons of RRBF neural ***,to ensure the accuracy of SR-RBF neural network,the parameters are trained by improved LM *** SR-RBF neural network is used for approximating the time-series prediction and classical non-linear ***,comparisons with other methods demonstrate that the SR-RBF neural network is more effective in terms of accuracy,generalization,and network structure.

关键词： Self-organizing spiking mechanism recurrent radial basis function time-series prediction

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Cognitive Navigation for Intelligent Mobile Robots:A Learning-Based Approach With Topological Memory Configuration

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1933-1943页

作者： Qiming Liu Xinru Cui Zhe Liu Hesheng Wang Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China MoE Key Laboratory of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong UniversityShanghai 200240China

Autonomous navigation for intelligent mobile robots has gained significant attention,with a focus on enabling robots to generate reliable policies based on maintenance of spatial *** this paper,we propose a learning-based visual navigation pipeline that uses topological maps as memory *** introduce a unique online topology construction approach that fuses odometry pose estimation and perceptual similarity *** tackles the issues of topological node redundancy and incorrect edge connections,which stem from the distribution gap between the spatial and perceptual ***,we propose a differentiable graph extraction structure,the topology multi-factor transformer(TMFT).This structure utilizes graph neural networks to integrate global memory and incorporates a multi-factor attention mechanism to underscore elements closely related to relevant target cues for policy *** from photorealistic simulations on image-goal navigation tasks highlight the superior navigation performance of our proposed pipeline compared to existing memory *** validation through behavior visualization,interpretability tests,and real-world deployment further underscore the adapt-ability and efficacy of our method.

关键词： Graph neural networks(GNNs) spatial memory topological map visual navigation

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Multi-objective Design of the Water Distribution systems Using SPEA2 35

Multi-objective Design of the Water Distribution Systems Usi...

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第35届中国控制会议

作者： MENG Qinchao QIAO Junfei YANG Cuili College of Electronic Information and Control Engineering Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781509009107

As a complicated and combinatorial optimization problem, the design of Water distribution systems(WDSs) is difficult to be solved. In this paper, in order to design WDSs better, three objectives are considered, including the initial construction cost, the sum of node surplus head and the variance of node surplus head. The consideration of the variance of node surplus head can reflect the distribution of the node surplus head in WDSs. This problem is solved by the strength pareto evolutionary algorithm(SPEA2). Finally, the model is applied into two well-known benchmark case studies, the two-loop network and the New York Tunnels network. By comparing with some algorithms, such as Tabu search algorithm and non-dominated sorting genetiv algorithm(NSGA2), the performance of SPEA2 shows the effectiveness in terms of reliability, convergence, as well as diversity.

关键词： Multi-objective optimization Water distribution systems SPEA2

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