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Reconstruction-Based Hand-Eye Calibration Using Arbitrary Objects

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第5期19卷 6545-6555页

作者： Xing, Shiyu Jing, Fengshui Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article introduces a flexible hand-eye calibration technique for a 3-D sensor from a reconstruction perspective, with no need for a specialized and accurate calibration rig. Our intention is to find the hand-eye relation that simultaneously aligns multiview point clouds of a common scene into the robot base frame, namely simultaneous calibration and reconstruction. To achieve this goal, a novel variant of iterative closest point (ICP) algorithm based on the Gauss-Newton method and Lie algebra is proposed, which iteratively transforms multiview point clouds into the robot base frame, estimates point-to-point correspondences between point clouds then refines the hand-eye relation to minimize the Euclidean distance between corresponding points. In addition to the calibration result, it returns a preliminary reconstruction as a byproduct. Cases of degeneracy and applicable conditions are given and proved. Using arbitrary daily objects with no prior information and a real robotic eye-in-hand system, we verify our method feasible and effective.

关键词： Robot sensing systems Calibration Robots Point cloud compression Three-dimensional displays Service robots Robot motion 3-D reconstruction arbitrary objects computer vision and its industrial application hand-eye calibration iterative closest point (ICP) variant

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OS-DS tracker: Orientation-variant Siamese 3D tracking with Detection based Sampling

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PATTERN RECOGNITION LETTERS 2023年第1期174卷 57-63页

作者： Zhang, Qiuyu Wang, Lipeng Li, Wanyi Meng, Hao Zhang, Wen Zhang, Zhi Wang, Peng Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

LIDAR point clouds are less affected by the weather and possess more depth of field than 2D images. However, sparsity and disorder of point clouds bring challenges for 3D object tracking due to the difficulty in detecting and encoding. In addition, ineffective candidate proposals increase the phenomenon of the loss of target tracking or wrong target tracking. In this paper, we propose an Orientation-variant Siamese 3D object tracking network that utilizes a Detection based Sampling module to generate candidate proposals (OS-DS tracker). The Detection based Sampling module is used to cut the excessive and useless proposals. And the multivariate Gaussian sampling generates the candidate proposals when the objects are not detected. Concretely, we first pre-train PointRCNN Network to globally detect objects from LIDAR point clouds. Then the 3D detected objects are refined by Candidate Regions Sampling to generate candidate proposals. Meanwhile, to make the feature vectors more discriminative, we design an Orientation-variant Siamese Auto-encoder, i.e., tracking loss regress to the intersection over union (IOU) of the template box and the candidate region boxes. Our method is tested on the KITTI dataset and the SHIP Tracking dataset. Compared with the previous state-of-the-arts, the proposed method outperforms in vessel tracking with 5.2%/2.1% improvement in Success and Precision.

关键词： LIDAR point clouds Object tracking Gaussian sampling Candidate regions Auto-encoder

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Robust Monitor for Industrial IoT Condition Prediction

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IEEE INTERNET OF THINGS JOURNAL 2023年第10期10卷 8618-8629页

作者： Zhang, Xingwei Tian, Hu Zheng, Xiaolong Zeng, Daniel Dajun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

The robustness of machine learning (ML) models has gained much attention along with their wide application on various safety-required Industrial Internet of Things (IIoT) paradigms. Researchers found that some specific attacks added on sensor measurements can maliciously disturb IIoT monitors that are designed using ML architectures. The Traditional detection methods could judge whether the measurements are attacked to prevent the failure of monitors. Unfortunately, recent works argue that the commonly used detection methods could be circumvented through adaptive attacks that could acquire the mechanism of detectors;they could not truly enhance the robustness of ML models. Instead, general robust mechanisms should be performed to authentically enhance the robustness of models against any potential attacks with specific restrictions. On the basis of the above argument, we design a robust condition monitor for predicting the fault condition of IIoT systems using the adversarial training technique called robust temporal convolutional network (RTCN). The model is designed to be formally robust to attacks with restricted magnitude. The temporal convolutional network (TCN) is employed to design the base structure of the monitor. TCN can capture temporal information from sensors to enhance the feature extraction performance of models. We also present a novel false data injection (FDI) attack-generating method that utilizes the conception of adversarial perturbations to disturb well-trained monitors. The experimental results verify the efficiency of feature extraction performance of our model from IIoT systems. Furthermore, adversarial training mechanism through a min-max manner could effectively improve the reliability of ML-based IIoT monitors against strong FDI attacks.

关键词： Perturbation methods Monitoring Industrial Internet of Things Training Predictive models Robustness Temperature sensors Adversarial perturbation adversarial training Industrial Internet of Things (IIoT) machine learning (ML) temporal convolutional network (TCN)

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A Tremor Suppression and Noise Removal Algorithm for Microscopic Robot-Assisted Cataract Surgery

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第5期28卷 2941-2952页

作者： Bian, Gui-Bin Wang, Jie Fu, Pan Wei, Bing-Ting Li, Ming-Jun Li, Zhen Beijing Informat Sci & Technol Univ Sch Automat Beijing 100101 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Ophthalmic surgery consists of a series of precise manipulations performed using a surgical microscope. A surgical robot with a master-slave paradigm is commonly adopted to improve the precision and guidance intelligence for ophthalmic surgery;however, the surgeon's hand tremor, misoperation, inherent characteristics of the robot, and environmental interference may cause tremors of the end-effector of the surgical manipulator, thus increasing the risk of soft tissue damage and poor prognosis for the patient. A symmetrical threshold noise reduction combiner (STIC) is proposed to suppress hand tremors and reduce environmental noise interference. Additionally, the inverse kinematic model of the eight-degrees-of-freedom surgical robot for cataract surgery is developed, with the constraints of continuous circular capsulorhexis manipulation. Simulation results show that the signal-to-noise ratio of the STIC is increased from 35.3907 to 57.0158 dB, whereas the maximum, average, standard deviation, and root mean square of the output of the STIC are decreased by 64.79%, 77.24%, 74.50%, and 75.86%, respectively. Three sets of in vitro experimental results show that these four indicators of the STIC performance are reduced by 59.73%, 76.57%, 57.23%, and 65.02%, respectively. These results validate the feasibility and effectiveness of the proposed method on hand tremor suppression and inherent high-frequency disturbance elimination, thereby enhancing the accuracy of the microsurgical operation.

关键词： Cataract capsulorhexis constraint in vitro experiment microscopic surgical robot noise removal teleoperation tremor suppression

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Scenarios Engineering for Trustworthy AI: Domain Adaptation Approach for Reidentification With Synthetic Data

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IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2024年第11期54卷 6901-6910页

作者： Li, Xuan Wang, Xiao Deng, Fang Wang, Fei-Yue Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Peng Cheng Lab Dept Strateg & Intersecting Frontiers Shenzhen 518000 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230031 Peoples R China Beijing Inst Technol Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Reidentification (Re-ID) is a crucial computer vision application with a variety of potential uses in many maritime scenarios, including search, rescue, and surveillance. However, the development of advanced boat reidentification (Boat Re-ID) algorithms necessitates the availability of large-scale Re-ID datasets for model training and evaluation. Inspired by scenarios engineering, this study proposes a new framework for automatically generating a realistic synthetic dataset for boat Re-ID investigation. The synthetic dataset contains 107 boat models and various visual conditions in 36 real backgrounds. The use of synthetic datasets enables the learning-based Re-ID algorithm's performance to be quantitatively verificated under varying imaging conditions. Nonetheless, our experiments prove that synthetic datasets are inadequate to handle real-world challenges. Therefore, we present a domain adaptation approach that integrates both real and synthetic data to create trustworthy models. This approach employs a multistep training strategy, gradient reversal layer and novel loss functions to preserve the features from two distribution dataset domains. The results of the experiments demonstrate that 1) synthetic datasets can be employed to train boat Re-ID algorithms and quantitatively test the performance of these algorithms under diverse imaging conditions and 2) our approach utilizes the attributes of the two data domains (real and synthetic) to achieve exceptional performance in real-world applications.

关键词： Boats Synthetic data Training labeling Deep learning Feature extraction Data models Boat reidentification (Boat Re-ID) domain adaptation (DA) scenarios engineering (SE) synthetic dataset unmanned aerial vehicles (UAVs)

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Hashing Fake: Producing Adversarial Perturbation for Online Privacy Protection Against Automatic Retrieval Models

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systemS 2023年第6期10卷 3241-3251页

作者： Zhang, Xingwei Zheng, Xiaolong Mao, Wenji Zeng, Daniel Dajun Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China

The wide application of deep neural networks (DNNs) has significantly improved the performance of hashing models on multimodal retrieval issues. DNN-based deep models can automatically learn semantic features from raw data to make human-level decisions. However, the superior generalization leads to potential privacy leakage risks. Strong DNN-based retrieval models enable malicious crawlers to search for nontag private information based on semantic similarity matching. Hence, executing effective privacy protection mechanisms against those retrieval software is essential for reliable social website construction. In this article, we propose a retrieval task-based adversarial perturbation generation method called Hashing Fake to meet this request. Specifically, DNNs are recently found to be vulnerable to a specific set of attacks called adversarial perturbations, which denote some magnitude-restricted signals added on objective samples to misguide well-crafted DNN models, and perturbations' magnitudes are small enough that will not induce humans' perception. Moreover, since existing adversarial perturbation generation methods are designed for supervised tasks, Hashing Fake constructs a differential approximation substitution for perturbation production on unsupervised retrieval tasks. Through extensive experiments on several deep retrieval benchmarks, we demonstrate that well-crafted perturbations using Hashing Fake can effectively misguide objective models' recognitions to make false predictions. The added norm-restricted perturbations on objective samples will not alter humans' perception;hence, Hashing Fake can be applied on real-world social websites to protect subscribers' privacy against malicious retrieval software.

关键词： Perturbation methods Semantics Computational modeling Codes Task analysis Privacy Software Adversarial perturbation deep neural network (DNN) privacy protection semantic retrieval

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A Cerebellum-Inspired Prediction and Correction Model for Motion control of a Musculoskeletal Robot

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systemS 2023年第3期15卷 1209-1223页

作者： Zhang, Jinhan Chen, Jiahao Wu, Wei Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

It is an important issue that how to regulate the existing control models of musculoskeletal robots to improve the ability of motion learning and generalization. In this article, based on the motion modulation function of the cerebellum, a cerebellum-inspired prediction and correction model is proposed to carry out feedforward regulation of the original controller. First, drawing on the reservoir computing mechanism of the cerebellar granular layer, the cerebellum prediction model is established by using the echo state network. Incremental learning for the network is achieved using the replay method, which is able to process control signals with different distributions. The cerebellum prediction network can accurately predict the motion results of the robot under the action of the time-series control signals. Second, referring to the neural pathways of the cerebellum, the cerebellum correction model is constructed. The network learning rules are designed by drawing on the long-term potentiation and depression processes of cerebellar synaptic plasticity. The characteristics of the parameters in the network weight update equations are further analyzed. And the hyperparameter update rules of the correction network weights are proposed. To simulate the function of the cerebellum involved in the limb ballistic movement, the adaptive adjustment method for cerebellum correction duration is proposed. The cerebellum correction network can accurately modulate the original control signals by using the motion prediction results. In experiments, a musculoskeletal robot is used to verify the movement effects under the control of the cerebellum model. The results show that the cerebellum-inspired model can effectively improve the motion accuracy and learning efficiency of the musculoskeletal robot, and enhance the motion generalization ability and system robustness of the robot.

关键词： Biologically inspired control cerebellum-inspired model motion generation motion learning musculoskeletal robot reinforcement learning

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Distributed Dynamic Event-Triggered control for Euler-Lagrange Multiagent systems With Parametric Uncertainties

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第2期53卷 1272-1284页

作者： Cao, Ran Cheng, Long Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article investigates the distributed dynamic event-triggered control of networked Euler-Lagrange systems with unknown parameters. Using the designed dynamic event-triggered control algorithm, the leaderless consensus problem and the containment problem of networked Euler-Lagrange systems are solved, and the estimations of unknown parameters are updated by an adaptive updating law as well. The stability analysis is given based on an appropriate Lyapunov function and the distributed control problem is theoretically solved by the designed control algorithm. The Zeno behavior of the designed dynamic event-triggered method is excluded in a finite-time interval. Compared to some existing results for the event-triggered control of networked Euler-Lagrange systems, these event-triggered methods can be seen as the special cases of the dynamic event-triggered method proposed in this article. Simulation results based on UR5 robots of V-rep show that the proposed method can provide an increase (4.46 +/- 3.36%) of the average lengths of event intervals compared to the one of the existing event-triggered methods, which leads to a lower usage of the communication resource. Meanwhile, the time of achieving the consensus/containment and the steady-state control performance are not affected.

关键词： Vehicle dynamics Nonlinear dynamical systems Symmetric matrices Laplace equations Heuristic algorithms Directed graphs Robot sensing systems Consensus containment dynamic event-triggered control Euler-Lagrange system multiagent systems (MASs)

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A feedforward compensation approach for cable-driven musculoskeletal systems

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ROBOTICA 2023年第4期41卷 1221-1230页

作者： Fan, Yerui Yuan, Jianbo Wu, Yaxiong Qiao, Hong Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

关键词： biomimetic robots control of robotic systems force control humanoid robots serial manipulator design and kinematics

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Multitasking bi-level evolutionary algorithm for data-intensive scientific workflows on clouds

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EXPERT systemS WITH APPLICATIONS 2024年第PartB期238卷

作者： Cai, Xingjuan Li, Mengxia Zhang, Yan Zhao, Tianhao Zhang, Wensheng Chen, Jinjun Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn Melbourne Australia

With the deployment of workflow and other applications, cloud computing is accessible and offers assistance for optimizing workflow execution and enhancing performance. Existing research, however, tends to disregard the influence of dataset migration on workflow execution and focuses more on task execution time. This study suggests a new model for the problem of data-intensive workflow execution. Firstly, according to the structure of the workflow scheduling problem, it is divided into two sub-problems: data placement and task scheduling. The two sub-problems interact with each other and a bi-level optimum model is established. By seeking a better allocation strategy for the dataset placement and then seeking the best task-scheduling solution. Secondly, an improved multitasking bi-level evolutionary algorithm (IM-BLEA) is proposed. When dealing with the lower-level optimization problem (LLOP), offspring are selected by sorting individuals by their performance and overall performance in the population, and this environmental selection enhances the diversity and searchability of the population. Finally, compared with the other multitasking algorithm, IM-BLEA has good performance. Simulation results based on real scientific workflows show that the algorithm improves the values of transfer time and number of selected data centers by 56% and 10% compared to the comparison algorithm.

关键词： Evolutionary multitasking algorithms Bi-level optimization Data-intensive scientific workflow Data placement Task scheduling

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