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42nd Chinese control Conference, CCC 2023

作者： Zhou, Yanling Zhang, Xuebo Wang, Runhua Zhang, Shiyong Yuan, Mingxing Yuan, Jing Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin300350 China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education China

ISBN: (纸本)9789887581543

Three-dimensional(3D) environmental reconstruction of the manipulator workspace is a prerequisite to enable autonomous collision-free path planning and subsequent task execution, wherein the positioning accuracy of the equipped sensor is one key issue to determine the performance of 3D reconstruction. In this paper, an eye-in-hand RGB-D camera is used to complete the manipulator workspace scanning and a novel high-accuracy pose estimation method for 3D reconstruction is proposed, which not only considers the geometric and photometric constraints constructed from the visual information used in traditional methods, but also considers the manipulator motion constraints since the positioning accuracy of the manipulator is usually high. Based on above three constraints, three error functions are constructed respectively and fused with different weights to form an integrated objective function. Then, the problem is converted into solving the least squares problem by the Gaussian Newton nonlinear iterative method, which involves the computation of Jacobian matrices and residuals of the three error functions. To this end, the mathematical derivation of their formulas considering the manipulator motion constraints are given in detail. By qualitative and quantitative experimental comparison with traditional methods, the proposed method can greatly improve the accuracy of pose estimation and 3D reconstruction, which always maintains the error within 3 cm, and show good robustness even in the challenging environments with weak texture or geometric features. (Supplemented video link: https://***/FkFTxssnWms) © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Manipulators

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Robust optimization-based fusion of GNSS and Visual-Inertial-Wheel Odometry

Robust optimization-based fusion of GNSS and Visual-Inertial...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Hou, Jake Jie Wu, Xinrui Shan, Jiwei Li, Dun Wang, Hesheng Shanghai Jiao Tong University Department of Automation China Shanghai Karlang Industrial Nantong Intelligent Technology Co. Ltd China Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

SLAM is significant to autonomous mobile robots. In order to realize drift-free state estimation and provide accurate localization results for the subsequent navigation, we develop an optimization-based framework consisting of a high-frequency Inertial-Wheel Odometry(IWO), Visual-Inertial-Wheel Odometry(VIWO) and a global fusion module. To fuse the local state with the position provided by GNSS, we perform ternary edge in factor graph optimization to estimate the transformation between the local frame and ENU coordinate. Thanks to the tightly-coupled VIWO, the localization property of the odometry presents more robustness, and improve the accuracy of global fusion. A noise repropagation strategy is introduced to ensure the consistency of the IMU and wheel odometer. We evaluate the proposed system on public datasets and test it in real-world scenario. The experimental results show that our system achieves accurate pose estimation in outdoor environments. © 2022 IEEE.

关键词： Frequency estimation

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Chaos Raman distributed optical fiber sensing

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Light(Science & Applications) 2023年第10期12卷 2105-2121页

作者： Chenyi Wang Jian Li Xinxin Zhou Zijia Cheng Lijun Qiao Xiaohui Xue Mingjiang Zhang College of Physics Taiyuan University of TechnologyTaiyuan 030024 ShanxiChina Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education Taiyuan University of TechnologyTaiyuan 030024 ShanxiChina College of Electrical Information and Optical Engineering Taiyuan University of TechnologyTaiyuan030024ShanxiChina Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering Taiyuan 030032 ShanxiChina

The physics principle of pulse flight positioning is the main theoretical bottleneck that restricts the spatial resolution of the existing Raman distributed optical fiber sensing *** to the pulse width of tens of nanoseconds,the spatial resolution of the existing Raman distributed optical fiber sensing scheme with kilometer-level sensing distance is limited to the meter level,which seriously restricts the development of the optical time-domain reflection *** this paper,a chaos laser is proposed in the context of the physical principle of the Raman scattering effect,and a novel theory of chaos Raman distributed optical fiber sensing scheme is *** scheme reveals the characteristics of chaos Raman scattering light excited by a chaotic signal on the sensing ***,the chaos time-domain compression demodulation mechanism between the temperature variation information and chaos correlation peak is ***,the position of the temperature variation signal is precisely located using the delay time of the chaos correlation peak combined with the chaos pulse flight *** on this novel optical sensing mechanism,an experiment with 10 cm spatial resolution and 1.4 km sensing distance was conducted,and the spatial resolution was found to be independent of the sensing *** the limit of the existing spatial resolution theory,the spatial resolution of the proposed scheme is 50 times higher than that of the traditional *** scheme also provides a new research direction for optical chaos and optical fiber sensing.

关键词： fiber optical scattering

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SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Tasks

SoftNeRF: A Self-Modeling Soft Robot Plugin for Various Task...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Qiyu Feng Ditao Li Lijun Han Hesheng Wang 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 Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Building a self-model for robots, enabling them to simulate their physical selves and predict future states without direct interaction with the physical world, is crucial for robot motion planning and control. Existing self-modeling methods primarily focus on rigid robots and typically require significant time, effort, and resources to gather training data. In this study, we introduce SoftNeRF, a self-supervised visual self-model designed for soft robots. We use a hybrid neural shape representation based on the Signed Distance Function (SDF) to capture both the geometry and complex nonlinear motion of soft robots. By leveraging differentiable rendering, our method learns a self-model from readily available RGB images, similar to how humans understand their physical state through reflection. To improve training efficiency and model accuracy, we propose an error-guided adaptive sampling strategy. SoftNeRF can serve as a plug-in for various downstream tasks, even when trained with data unrelated to those tasks. We demonstrate SoftNeRF’s ability to support shape prediction and motion planning for robots in both simulated and real-world environments. Furthermore, SoftNeRF excels in detecting and recovering from damage, thereby enhancing machine resilience. Code is available at: https://***/irmvlab/soft-nerf.

关键词： Training Visualization Adaptation models Accuracy Shape Training data Soft robotics Rendering (computer graphics) Sampling methods Planning

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DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscopic Scenes

DDS-SLAM: Dense Semantic Neural SLAM for Deformable Endoscop...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Jiwei Shan Yirui Li Lujia Yang Qiyu Feng Lijun Han Hesheng Wang 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 Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Estimating camera motion and continuously reconstructing dense scenes in deformable environments presents a complex and open challenge. Many existing approaches tend to rely on assumptions about the scene’s topology or the nature of deformable motion. However, these assumptions do not hold true in medical endoscopy applications. To address these challenges, we introduce DDS-SLAM, a novel dense deformable semantic neural SLAM that achieves accurate camera tracking, continuous dense scene reconstruction, and high-quality image rendering in deformable scenes. First, we propose a novel hybrid neural scene representation method capable of capturing both natural and artificial deformations. Additionally, by leveraging the 2D semantic information of the scene, we introduce a semantic loss function based on semantic distance fields. This approach guides network optimization at a higher level, thereby enhancing system performance. Furthermore, we validate our method through a series of experiments conducted on several representative medical datasets, demonstrating its superiority over other state-of-the-art approaches. The code is available at: https://***/IRMVLab/DDS-SLAM.

关键词： Simultaneous localization and mapping Tracking system performance Semantics Cameras Rendering (computer graphics) Topology Image reconstruction Optimization Biomedical imaging

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An Unsupervised Person Search Method for Video Surveillance 22

An Unsupervised Person Search Method for Video Surveillance

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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Feng, Deying Yang, Jie Wei, Yanxia Xiao, Hairong Zhang, Laigang School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Liaocheng University China

ISBN: (纸本)9781450396110

We propose an unsupervised person search method for video surveillance. This method considers both the spatial features of persons within each frame and the temporal relationship of the same person among different frames. Thus, the spatial features are extracted by region convolutional neural network, and the temporal relationship is organized by gate recurrent unit. The spatio-temporal features are generated by the following average pooling layer and indexed by locality sensitive hashing. A surveillance video database is constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves the search accuracy by utilizing the spatio-temporal features. © 2022 ACM.

关键词： Monitoring

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Unidentifiability of system Dynamics: Conditions and controller Design

arXiv

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arXiv 2023年

作者： Mao, Xiangyu He, Jianping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

How to make a dynamic system unidentifiable is an important but still open issue. It not only requires that the parameters of the systems but also the equivalent systems cannot be identified by any identification approaches. Thus, it is a much more challenging problem than the existing analysis of parameter identifiability. In this paper, we investigate the problem of dynamic unidentifiability and design the controller to make the system dynamics unidentifiable. Specifically, we first define dynamic unidentifiability by taking both system parameters and equivalent systems into consideration. Then, we obtain the necessary and sufficient condition for unidentifiability based on the Fisher information Matrix. This condition is derived by analysis of the relationship between the unidentifiable parameters and the Hessian matrix of the system function. Next, we propose a controller design scheme for ensuring dynamic unidentifiability under linear system models. We prove that for controllable and observable linear time-invariant systems, the requirement of unidentifiability is equivalent to the requirement of a low-rank controller. Then, the low-rank controller design problem is solved by transforming it into a model order reduction problem. We demonstrate the effectiveness of our method by simulation. Copyright © 2023, The Authors. All rights reserved.

关键词： controllers

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Robust Probabilistic Prediction for Stochastic Dynamical systems

arXiv

引用

arXiv 2023年

作者： Xu, Tao He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

It is critical and challenging to design robust predictors for stochastic dynamical systems (SDSs) with uncertainty quantification (UQ) in the prediction. Specifically, robustness guarantees the worst-case performance when the predictor’s information set of the system is inadequate, and UQ characterizes how confident the predictor is about the predictions. However, it is difficult for traditional robust predictors to provide robust UQ because they were designed to robustify the performance of point predictions. In this paper, we investigate how to robustify the probabilistic prediction for SDS, which can inherently provide robust distributional UQ. To characterize the performance of probabilistic predictors, we generalize the concept of likelihood function to likelihood functional, and prove that this metric is a proper scoring rule. Based on this metric, we propose a framework to quantify when the predictor is robust and analyze how the information set affects the robustness. Our framework makes it possible to design robust probabilistic predictors by solving functional optimization problems concerning different information sets. In particular, we design a class of moment-based optimal robust probabilistic predictors and provide a practical Kalman-filter-based algorithm for implementation. Extensive numerical simulations are provided to elaborate on our results. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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OPTIMAL MIXED STRATEGIES TO THE ZERO-SUM LINEAR DIFFERENTIAL GAME

arXiv

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arXiv 2023年

作者： Xu, Tao Xi, Wang He, Jianping Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

This paper exploits the weak approximation method to study a zero-sum linear differential game under mixed strategies. The stochastic nature of mixed strategies poses challenges in evaluating the game value and deriving the optimal strategies. To overcome these challenges, we first define the mixed strategy based on time discretization given the control period δ. Then, we design a stochastic differential equation (SDE) to approximate the discretized game dynamic with a small approximation error of scale O(δ2) in the weak sense. Moreover, we prove that the game payoff is also approximated in the same order of accuracy. Next, we solve the optimal mixed strategies and game values for the linear quadratic differential games. The effect of the control period is explicitly analyzed when the payoff is a terminal cost. Our results provide the first implementable form of the optimal mixed strategies for a zero-sum linear differential game. Finally, we provide numerical examples to illustrate and elaborate on our results. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Observation-based Optimal control Law Learning with LQR Reconstruction

arXiv

引用

arXiv 2023年

作者： Qu, Chendi He, Jianping Duan, Xiaoming Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

Designing controllers to generate various trajectories has been studied for years, while recently, recovering an optimal controller from trajectories receives increasing attention. In this paper, we reveal that the inherent linear quadratic regulator (LQR) problem of a moving agent can be reconstructed based on its trajectory observations only, which enables one to learn the optimal control law of the agent autonomously. Specifically, the reconstruction of the optimization problem requires estimation of three unknown parameters including the target state, weighting matrices in the objective function and the control horizon. Our algorithm considers two types of objective function settings and identifies the weighting matrices with proposed novel inverse optimal control methods, providing the well-posedness and identifiability proof. We obtain the optimal estimate of the control horizon using binary search and finally reconstruct the LQR problem with above estimates. The strength of learning control law with optimization problem recovery lies in less computation consumption and strong generalization ability. We apply our algorithm to the future control input prediction and the discrepancy loss is further derived. Numerical simulations and hardware experiments on a self-designed robot platform illustrate the effectiveness of our work. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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