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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Chai, Yu Yang, Chen Dong, Huijuan Zhao, Jie Shardt, Yuri A. W. Ju, Bingfeng ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China Technical University of Ilmenau Department of Automation Engineering Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798331529147

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%. © 2024 IEEE.

关键词： Predictive control systems

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Human Detection Combining RGB and Thermal Modalities

Human Detection Combining RGB and Thermal Modalities

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Chaymaa Fakrane Kennedy O. S. Mota Cristiano Premebida Rui P. Rocha Dept. of Electrical and Computer Engineering (DEEC) Institute of Systems and Robotics (ISR) University of Coimbra Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper addresses a late-fusion approach that combines RGB and thermal (long-wave IR) modalities to enhance human detection in indoor environments for robotic perception domains. The study explores multimodal human detection using YOLO series (v5s-6s and v8s-11s models), demonstrating significant improvements in detection accuracy under challenging lighting conditions on a dataset collected by a mobile robot. The use of a Weighted-Mean (WM) as a late-fusion approach demonstrates favorable results over more complex methods such as neural networks or SVM. The WM approach is simple to implement and has a low computational cost. Additionally, it is more interpretable and modular and allows straightforward adjustments to the weights of the YOLO models and thresholds, facilitating adaptation to the target system's specific needs. The experimental results indicate that the implemented late-fusion approach improves the baseline performance in terms of mean average precision.

关键词： YOLO Support vector machines Adaptation models Accuracy Computational modeling Neural networks Lighting Indoor environment Computational efficiency Mobile robots

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation 17

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics, ACFM 2023

作者： Xu, Xue-Han Liu, Jing-Cheng Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin300072 China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k-ϵ turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles. © The Institution of engineering & Technology 2023.

关键词： Flow fields

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A Probabilistic Framework Applied to Multispectral Image Segmentation

A Probabilistic Framework Applied to Multispectral Image Seg...

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Intelligent Reality (ICIR), IEEE International Conference on

作者： Wilgo Cardoso Tiago Barros Gil Gonçalves Cristiano Premebida University of Coimbra Institute of Systems and Robotics Portugal Dept. of Mathematics University of Coimbra Institute for Systems Engineering and Computers at Coimbra (INESC Coimbra) Portugal

ISBN: (数字)9798331534424

ISBN: (纸本)9798331534431

This paper describes a study on exploring probability densities applied to the logit values of deep models to obtain predictions with probabilistic interpretation. Kernel density estimation (KDE) is used to providing non-parametric modeling of the underlying logit-values distribution. While preliminary results show promising potential, this work lays the foundation for future research, where the combination of KDE with other advanced techniques could further enhance segmentation performance of deep models. This study reports segmentation results on a dataset collected by a UAV equipped with a multispectral camera flying over vineyards.

关键词： Measurement Image segmentation Estimation Predictive models Probabilistic logic Cameras Autonomous aerial vehicles Calibration Reliability Kernel

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FDNet: Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Fourier Series and DenseNet 5

FDNet: Remaining Useful Life Prediction for Lithium-Ion Batt...

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5th International Conference on Machine Learning and Computer Application, ICMLCA 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan China

ISBN: (纸本)9798331530334

To enhance the prediction accuracy of the remaining useful life (RUL) of lithium-ion batteries, this study proposes a novel RUL prediction model, termed FDNet, which integrates Discrete Fourier Series (DFS) and Densely Connected Convolutional Networks (DenseNet). In contrast to traditional methods that rely on one-dimensional (1D) numerical representations of time-series data, this model utilizes a two-dimensional (2D) visual representation for direct RUL prediction. First, the DFS is employed to convert 1D time-series signals into a 2D time-frequency domain, enabling the extraction of high-dimensional features from the original data. Then, these 2D images are subsequently input into DenseNet, which learns the mapping between the image features and the RUL targets. Finally, the proposed model is validated using the NASA lithium-ion battery aging dataset, and the results indicate that FDNet achieves superior prediction accuracy and exhibits notable robustness. © 2024 IEEE.

关键词： Prediction models

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HG-CBS Planner: Heuristic Group-based Motion Planning for Multi-robot 18

HG-CBS Planner: Heuristic Group-based Motion Planning for Mu...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Liu, Weilong Wang, Jin Zhang, Kewen Yu, Huan Zheng, Zhi Lu, Guodong State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China Robotics Institute of Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798350312201

Multi-Agent Path Finding (MAPF) has been widely studied in the field of artificial intelligence and multi-agent path search, and researchers have made improvements to make such algorithms applicable to actual nonholonomic constrained robots. In this paper, the collision detection is improved by body-occupied collision detection, which improves the efficiency of collision detection. In addition, to solve the problem of low efficiency in centralized search for multi-robot solutions, reasonable grouping is performed to reduce the number of collision nodes expanded in the centralized Conflict-based search(CBS), and feasible results are obtained faster. At the same time, the heuristic multi-robot search based on collision frequency is improved to reduce the search time. Finally, we constructed the trajectory smoothing problem based on the multi-robot path search results and applied the solution to real robot trajectory tracking. Our research provides valuable insights into solving trajectory planning problems for nonholonomic constrained mobile robots in practical scenarios. © 2023 IEEE.

关键词： Motion planning

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Estimation and Prediction of CoM With Terrain Feature Embedding During Walking

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IEEE Transactions on Automation Science and engineering 2025年 22卷 16108-16120页

作者： Haolan Xian Jingfeng Xiong Yuanwen Zhang Xinxing Chen Chenglong Fu Yuquan Leng Key Laboratory of Biomimetic Robotics and Intelligent Systems Shenzhen China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen China School of Biomedical Engineering and the State Key Laboratory of Robotics and Systems Harbin Institute of Technology (Shenzhen) Shenzhen China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China

Wearable devices are currently being used to reduce metabolism and assist people with disabilities for daily walking. For the elderly and disabled people, improving the walking stability of wearable devices is a crucial and unsolved research. In particular, the center of mass (CoM) trajectory can reflect the walking state as well as the stability of a person. For this reason, realizing the prediction of CoM trajectory under daily walking is promising to improve the wearable devices. In this article, a method for estimation and prediction of CoM during daily walking was proposed. A visual-inertial-odometry algorithm was used to obtain history CoM trajectories during walking, and the depth information from camera data was extracted by sequential distance embedding method and encode the information into terrain vectors. The trajectory vectors were patched with the corresponding terrain vectors and then realized the fusion of multi-modal data, which were then fed into a well-trained temporal convolution network to output the prediction results. With data from two outdoor datasets, the method in this paper was verified to be able to be used for CoM trajectory prediction for a variety of walking tasks and consume low computational cost. This method has the potential to be extensible in improving the stability of wearable devices, such as exoskeleton, powered prosthetic, and so on. Note to Practitioners—It is necessary to obtain the future state of human center of mass (CoM) to improve the stability of user with wearable devices. However, current methods for CoM trajectory prediction, especially with multi-modal data fusion are difficult to apply to this prediction task due to the high computational cost. This article addressed the problem of estimation and prediction of CoM under daily walking conditions and proposed a method to solve the above problems, which enable to deploy on wearable robots with limited computation resources. In our study, a visual-inertial-odometry

关键词： Trajectory Legged locomotion History Cameras Vectors Wearable devices Feature extraction Estimation Wearable robots Sensors

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A Novel Gas Classification Method Based on The Improved GasNet 43

A Novel Gas Classification Method Based on The Improved GasN...

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43rd Chinese Control Conference, CCC 2024

作者： Qin, Xiaolin Cheng, Lei Tao, Xiang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan430081 China Wuhan University of Science and Technology School of Information Science and Engineering Wuhan430081 China

ISBN: (纸本)9789887581581

Accurately and promptly identifying harmful gases in the environment can effectively prevent many accidents. Most existing pattern recognition methods are focused on addressing gas classification problems in static scenarios, but the real world is a dynamic and changing environment. Therefore, researching gas classification in continuous learning scenarios will better meet the needs of suddenly emerging gas classification tasks in real-world environments. To address this issue, an incremental learning model was introduced into gas classification, and an improved deep convolutional network was proposed for gas image feature extraction. The proposed model and other baseline methods experimented on an open-source gas dataset. The proposed model reduces knowledge loss due to catastrophic forgetting and effectively classifies the 10 gas samples in the dataset in continuous learning scenarios compared to non-incremental methods. It outperforms other baseline incremental methods by optimizing the network's shortcut connections, making the feature extraction network easier to train and improving gas classification performance. In dynamic learning scenarios, the proposed model achieved a classification accuracy of 86.30% for 10 gas samples. The experimental results demonstrate that the proposed method effectively mitigates catastrophic forgetting interference and completes gas classification tasks in continuous learning scenarios. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Contrastive Learning

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Modeling of the Constant-Current Stimuli Response of a Bio-Robot for Long-Term Motion Control

Modeling of the Constant-Current Stimuli Response of a Bio-R...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Jie Fang, Yuan Chen, Yang Li, Yao Li, Bing Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

ISBN: (纸本)9781665481090

Bio-robots continue to receive attention because of their small size and low power consumption. Unfortunately, it currently faces two major obstacles before practical application. The first is the habituation issue, insects are difficult to be effectively controlled for a long time by electrical stimuli signals. The other one is the lack of model for motion control. In this research, we established a stimuli-response model for cockroach bio-robot based on long time current. A more convenient optic lobe implantation method has been proposed, which has lower attenuation and better stimuli effect. Constant electrical stimuli also significantly increased the number of effective controls for crawling. A cockroach bio-robot stimuli distance response model was established and validated with experiment data. Five parameters were including in this model, namely sex, length, weight, current amplitude and equivalent stiumated-number. This model can predict the movement distance of biological robots well. © 2024 IEEE.

关键词： Microrobots

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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