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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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Robust control of space robot for capturing objects using optimal control method

Robust control of space robot for capturing objects using op...

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International Conference on Information Acquisition, ICIA 2007

作者： Huang, Panfeng Yan, Jie Yuan, Jianping Xu, Yangsheng Research Center for Intelligent Robotics College of Astronautics Northwestern Polytechnical University Xi'an China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)1424412196

The capturing operation for the space object by space robot can not be avoided in order to complete the on-orbital services, such as repairing, maintaining, inspecting and exchanging orbital replace unit (ORU). However, there are some unknown factors, such as the unknown payload and unknown modeling of object and the other uncertain dynamic parameters in the space robot, will affect the performance and the security of the space robot when the space robot captures the unknown object. Therefore, it is significant to design a robust control law that will guarantee the performance and security of the space robot under these uncertainties. Here, we propose a new optimal control method to robust control of space robot. To design the robust control law, we translate the robust control problem into an optimal control problem, where the uncertainties are reflected in the objective function index. We optimize this objective function to realize the robust control. An illustrative example shows that the proposed approach is remarkable. ©2007 IEEE.

关键词： Robust control

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Human Age Classification Using Appearance Images for Human-Robot Interaction

Human Age Classification Using Appearance Images for Human-R...

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Annual Conference of the IEEE Industrial Electronics Society

作者： Ren C. Luo Li Wen Chang Shih Che Chou Center for Intelligent Robotics and Automation Research National Taiwan University

ISBN: (纸本)9781479902255

There are many modern applications require the function of age classification. In this study, we propose a method to classify human age using appearance images and apply it to the human-robot interactions. We first confirm that facial features based on craniology are not discriminative under the condition of seven age-groups classification. Next, our system is designed to have two stages. One is image preprocess stage;faces are detected using Haar-like features with Adaboost algorithm. Our image database is from FG-NET and MORPH databases so that we have high degree of complexity and difficulty in recognition. Then images are trained by support vector machines (SVM). To have higher recognition rate, we train RBF (radial basis function) and linear kernel models at the same time, and decide the final results by comparing the two models. These improve the accuracy under age of 30 to 49 years old while the linearity is preserved under age of 0 to 29 and above 50 years old. The final age recognition rates achieve 91.5% for female and 96% for male. We also compare the age-group classification results with subjective questionnaires, and it demonstrates that the proposed system has better performance than human's subjective estimation.

关键词： Age-group Classification Support Vecter Machines (SVM) Appearance Images Subjective Questionnaires (SQ) Human-Robot Interaction (HRI) Human machine interaction Aged images visual databases Facial Features Support Vector Network Recognition (Psychology) Human Linearity

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Towards Disturbance Rejection in Feature Pyramid Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 946-958页

作者： Hu, Xiaobo Xu, Wei Gan, Yi Su, Jianbo Zhang, Jun Shanghai Jiao Tong University Joint Institute of UM-SJTU Shanghai200240 China Shanghai Jiao Tong University Research Center of Intelligent Robotics Key Laboratory of System Control and Information Processing Ministry of Education Department of Automation Shanghai200240 China

The multiscale problem is one of the challenging topics in the object detection field. Among the current studies toward this problem, feature pyramid network (FPN) has been shown a superior performance. The core principle of FPN is a mapping for the detectable objects into hierarchy features. These features are designed with different size of receptive fields, which brings good scale invariance in the classification and localization subtasks. However, a disturbance of information dilution is generated by the assignment for the detectable objects into different features. This dilution disturbance is associated with the convolution or pooling operation during the hierarchy feature extraction process. Both of the imprecise localization information in the top features and the fuzzy classification information in the bottom ones limit the performance of FPN. Aiming at the disturbance rejection problem of information dilution, this article presents a feature compensation method. A cross compensation between the semantic information and the localization information in hierarchy features is used to enhance the vanilla FPN. Then, the evaluation metrics in an object detection task are examined. Not only the overall performance, but also the respective metrics for multiscale objects validate a considerable improvement of the FPN with a disturbance compensation mechanism. The experimental results demonstrate an effectiveness of the proposed method in this article. © 2020 IEEE.

关键词： Object detection

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Goal recognition with noisy observations 31

Goal recognition with noisy observations

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31st AAAI Conference on Artificial Intelligence, AAAI 2017

作者： E-Martin, Yolanda Smith, David E. Centre for Automation and Robotics CSIC-UPM Madrid28500 Spain NASA Ames Research Center Intelligent Systems Division Moffett FieldCA94035-1000 United States

ISBN: (纸本)9781577357865

Goal recognition is an important technological capability for applications that involve cooperation between agents. Many goal recognition techniques allow the sequence of observa-tions to be incomplete, but few consider the possibility of noisy observations. In this paper, we describe a planning- based goal recognition approach that deals with both missing observations and probabilistic noise in the observations. To do this, we first use a Bayesian network to infer action probabilities based on the observation model and the current observation sequence. We then use this information to estimate the expected cost of reaching the different possible goals. Comparing these costs to the a priori costs for the goals allows us to infer a probability distribution over the possible goals. © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Bayesian networks

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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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Fractional-Order Global Fast Terminal Sliding Mode Control Strategy for Fractional-Order Hydraulic Turbine Regulating System 42

Fractional-Order Global Fast Terminal Sliding Mode Control S...

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42nd Chinese Control Conference, CCC 2023

作者： Xi, Shiyu Chen, Zhihuan Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9789887581543

Hydraulic turbine regulating system (HTRS) directly affects the safety and stability of hydro-generator units. In this paper, the control and modeling problems of the HTRS are mainly investigated based on the fractional-order theory, aiming to design a novel control strategy to raise the dynamic response of the HTRS and ensure the frequency stability of the hydro-generator units. Firstly, a nonlinear model of the fractional-order HTRS is established mathematically. Secondly, a fractional-order global fast terminal sliding mode controller is designed to improve the dynamic performance as well as the robustness of the system. Finally, relevant simulations are carried out to illustrate the effectiveness and rationality of the proposed control strategy in comparison with two other fractional-order control strategies. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Hydraulic motors

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Stackelberg game-based optimal scheduling strategy for multi-microgrid systems with electricity-heat shared energy storage

Stackelberg game-based optimal scheduling strategy for multi...

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2024 China automation Congress, CAC 2024

作者： Peng, Chao Zhang, Decai Hu, Mian Wu, Huaiyu Chen, Yang Engineering Research Center for Metallurgical Automation and Measurement Technology Ministry of Education Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China

ISBN: (纸本)9798350368604

To address the challenges of the complexity of electricity-heat coupling, the pollution of carbon emissions, the security of privacy information, and the uncertainty of renewable energy generations (REGs), this paper proposes a novel multi-microgrid electricity-heat shared energy storage system framework, considering that the integrated energy storage operator (IESO) is equipped with electric boiler and two types of energy storage (i.e., electrical energy storage and heat energy storage) devices, and each microgrid (MG) is equipped with combined heat and power system that coupled carbon capture system and power-to-gas device. Latin hypercube sampling is used to deal with the uncertainty of REGs. A distributed bi-level iterative algorithm based on Stackelberg game theory is designed, which effectively protects the users’ privacy information, and finally a Stackelberg equilibrium strategy is obtained, where the IESO and each MG minimize its operating cost. Simulation results show that the proposed optimal scheduling strategy can effectively reduce the operating cost and carbon emissions of each MG, and increase the benefits of the IESO. © 2024 IEEE.

关键词： Low emission

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Multiple face contour detection using adaptive flows

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 3338卷 137-143页

作者： Huang, Fuzhen Su, Jianbo Institute of Automation Research Center of Intelligent Robotics Shanghai Jiaotong University Shanghai 200030 China

A novel method based on a curve evolution approach using adaptive flows is presented for multiple face contour detection in grayscale images. Assuming that the image consists of an arbitrary unknown number of separated faces and a uniform background region, the proposed method adaptively estimates mean intensities for each separated region and use a single curve to capture multiple regions with different intensities. The adaptive flow developed in this paper is easily formulated and solved using level set method. Experiments are performed on a large test set and show the efficiency of our method. © Springer-Verlag Berlin Heidelberg 2004.

关键词： Numerical methods

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Confidence Fusion Based Emotion Recognition of Multiple Persons for Human-Robot Interaction

Confidence Fusion Based Emotion Recognition of Multiple Pers...

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IEEE/RSJ International Conference on intelligent robotics and Systems

作者： Ren C. Luo Pei Hsien Lin Li Wen Chang International Center of Excellence on Intelligent Robotics and Automation Research National Taiwan University

ISBN: (纸本)9781467317375

Emotional interaction with human beings is desirable for robots. In this study, we propose an integrated system which has ability to track multiple people at the same time, to recognize their facial expressions, and to identify social atmosphere. Consequently, robots can easily recognize facial expression, emotion variations of different people, and can respond properly. In our facial expression recognition scheme, we fuse Feature Vectors based Approach (FVA) and Differential-Active Appearance Model Features based Approach (DAFA) to obtain not only apposite positions of feature points, but also more information about texture and appearance. With the obtained useful information, FVA can classify the emotions according to comparison with the distances and ratios of feature points, and DAFA can distinguish emotions from classical machine learning on a low dimensional manifold space. Furthermore, emotion recognition of multiple people at the same time is extended. Based on the proposed algorithm, multiple person emotion analysis and social atmosphere identification can be achieved, which makes the relationship between people and robots much closer. Experimental results demonstrate that the proposed algorithms can recognize facial expressions accurately and robustly. The ambient atmosphere identification system is implemented with a young Einstein robot head in our laboratory.

关键词： Alignment and Tracking of Facial Features Active Appearance Model (AAM) Differential-AAM features (DAFs) Facial Expression Recognition Human-Computer Interactions Ambient Atmosphere Identification

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