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Frontiers of robotics and Software Engineering (FRSE), International Conference on

作者： Sitong Guo Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Key Laboratory of Collaborative sensing and autonomous unmanned systems of Zhejiang Province Hangzhou China

Animals can navigate robustly through large unknown environments, utilizing inaccurate sensor and egomotion cues. Inspired by the mechanism of the human visual system, this paper presents a biological simultaneous localization and mapping (SLAM) system based on a monocular vision sensor to perform navigation tasks in complex environments. Specifically, our contributions are two folds: an improved monocular visual odometry with a scale recovery algorithm to calibrate the estimated robot pose, and a bionic vision model based on image enhancement algorithm and feature extraction approach to detect closed-loop. Experiments on public datasets demonstrate that our proposed method performs competitively in illumination-varying scenarios indoors and outdoors, which achieve a trade-off to maintain performances in terms of accuracy, execution time, and robustness.

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Actor-Based Learning Framework for the Co-Optimization of Modular Cpps Scheduling and control with Stochastic Processing Times

SSRN

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SSRN 2023年

作者： Wan, Guangxi Dong, Xiaoting Dong, Qingwei He, Yunpeng Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Scheduling with stochastic processing times is a kind of dynamic scheduling in production systems. The current scheduling optimization problem is formulated in the scheduling domain, which ignores the underlying synergies for optimization objectives between scheduling and control. To address this problem, we propose and formulate the problem of the co-optimization of scheduling and control with stochastic processing times (COSC-SPT) with a system respective view. To solve the COSC-SPT, we model the decision-making process as a Markov decision process and propose an actor-based learning framework to facilitate problem solving. To verify the feasibility of the proposed framework and the effectiveness of the formulated COSC-SPT, a real reducer assembly system is taken as a use case. The results of experiments demonstrate that the COSC-SPT provides better solutions than conventional scheduling problems. keywords: Actor, cyber-physical production systems, optimization, scheduling, stochastic processing time. © 2023, The Authors. All rights reserved.

关键词： Markov processes

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph

A Decoupled and Linear Framework for Global Outlier Rejectio...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Tianyue Wu Fei Gao State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China

We propose a robust framework for planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated in rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity property of the angle representation, our framework requires only a linear solver to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC- LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.

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WindowMixer: Intra-Window and Inter-Window Modeling for Time Series Forecasting

arXiv

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arXiv 2024年

作者： Liu, Quangao Li, Ruiqi Jiang, Maowei Yang, Wei Liang, Chen Pang, LongLong Zou, Zhuozhang Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shengyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences China

Time series forecasting (TSF) is widely applied in various fields such as economic forecasting, weather prediction, traffic flow analysis, and public health surveillance. The core of TSF is to capture the underlying temporal patterns in the data. However, real-world time series data exhibit complex temporal patterns and may include additional noise, outliers, missing values, etc., making accurate forecasting highly challenging. Current mainstream methods typically naively treat each time point as an isolated entity, attempting to model point-to-point relationships and uncover temporal patterns within the data, which has limitations when dealing with complex time series data. Specifically, point-by-point modeling fails to take advantage of the abundant contextual information in time series data. This results in a reduced ability to capture temporal patterns and an increased susceptibility to noise. To address these issues, we examine temporal variations from a window-based perspective and present the WindowMixer model, which is built on an all-MLP framework. WindowMixer stems from a simple observation: Time series are recorded in a continuous manner, and the information at any given time point relies on the preceding and succeeding time points for a complete representation. WindowMixer decomposes time series into trend and seasonal components via a sequence decomposition and fusion architecture, handling them individually. For the trend component, we employ a straightforward fully connected (FC) layer to make predictions. For the seasonal component, we first introduce a time window at each time point and project each time window to produce window tokens. Then, we apply the Intra-Window-Mixer and Inter-Window-Mixer to further process these tokens. The Intra-Window-Mixer models relationships within each window token, while the Inter-Window-Mixer models relationships between window tokens. These dual modules allows us to capture more intricate patterns and long-range de

关键词： Time series

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Autonomous Trajectory Planning Based on Bézier Curve with Curvature Constraints and Piecewise-Jerk Speed-Time Optimization

Autonomous Trajectory Planning Based on Bézier Curve with C...

引用

IEEE International Conference on robotics and Biomimetics

作者： You Wang Ziyi Zou Ziang Zhang Xiaoqing Guan Boyu Lin Xiang Li Guang Li State Key Laboratory of Industrial Control Technology Institute of Cyber Systems and Control Zhejiang University Hangzhou China

In this paper, we propose an efficient trajectory planning algorithm with path smoothing based on the Bézier curve with curvature constraints and piecewise-jerk speed-time optimization. We use hybrid A* to generate a rough path and construct a safe corridor by inflating the path. After that, we formulate the smooth problem as a nonlinear programming(NLP) with piecewise Bézier curves. Since the curvature constraints for Bézier curves are difficult, we employ quartic Bézier Curves with special forms and compute the closed-form solution for the maximum curvature to simplify the representation of the maximum curvature. By using the special Bézier curves, we realize the gear shifts and easily guarantee the security, continuity, and feasibility of the path. Meanwhile, we add time variables based on PJSO, improving the quality of trajectory within an acceptable increase in time, making the allocation of time and speed better. Simulation and real-world experiments with a car-like robot in various environments confirm that our algorithm can generate a smooth, feasible, and high-quality trajectory for robots.

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Research on Optimization of Aluminum Electrolysis Product Tending Machine Queuing System with Non-preemptive Priority

Research on Optimization of Aluminum Electrolysis Product Te...

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2022 International Conference on Mechanisms and robotics, ICMAR 2022

作者： Song, Libo Shi, Haibo Liu, Chang Zhu, Jun Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781510657328

Product tending machine (PTM) is one of the most important equipment in the aluminum electrolysis production process, for the problem of PTM optimization, a queuing system model of aluminum electrolysis PTM is proposed for the first time. According to the actual production situation of the aluminum electrolysis industry, a non-preemptive priority M/G/1 aluminum electrolysis PTM queuing system model is defined, and the general calculation formula of the operating index of the non-preemptive priority PTM queuing system is given, including queuing system length, waiting time, optimal service rate, etc. According to the actual production data of an aluminum factory, the relevant indicators were calculated and analyzed, and the relationship between the operating indicators and the number of electrolysis cells was drawn. Finally, under the conditions of meeting different operating indicators, the optimal matching scheme of PTM and aluminum electrolysis cells was given. © 2022 SPIE.

关键词： Queueing theory

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Discrete particle swarm algorithm with Q-Learning for solving flexible job shop scheduling problem with parallel batch processing machine 2

Discrete particle swarm algorithm with Q-Learning for solvin...

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2022 2nd International Conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2022

作者： Song, Libo Liu, Chang Shi, Haibo Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

For the flexible job shop scheduling problem with parallel batch processing machines (FJSP-PBPM), the mathematical model of the problem is defined, and an improved enhanced discrete particle swarm algorithm is proposed to solve this problem. In terms of global search, an improved discrete particle swarm algorithm is adopted, and effective particle velocity and position update formulas are designed;in the local search, in order to speed up the convergence speed and avoid local convergence, it is integrated into the q-learning method in reinforcement learning. The state space, action space, reward function, and Q-table update methods that meet the scheduling problem are designed, and the proposed hybrid algorithm achieves an effective balance between global search and local search capabilities. At the same time, according to the characteristics of FJSP-PBPM, effective process sequence scheduling rules and batch machine selection rules are designed respectively to improve the search ability of the algorithm. Through the standard FJSP benchmark problem and the production data of a transformer manufacturer that meets the characteristics of the FJSP-PBPM problem, the validity and feasibility of the proposed algorithm are verified. © Published under licence by IOP Publishing Ltd.

关键词： Reinforcement learning

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The improved approach for calculating residual error rates in SIS communication 12

The improved approach for calculating residual error rates i...

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12th International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2022

作者： Zhou, Xiufang Xu, Aidong Yan, Bingjun Sun, Yue Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The residual error rate is an essential parameter for evaluating the functional safety of a communication system, based on the requirements of safety communication in the safety instrumented system (SIS). The traditional approach of quantifying residual error rates is incomplete in some cases. The proposed improvements focus on the different cases addressing full and partial redundancy models, time expectations and different verification approaches for connection identification. A hypergeometric distribution approach is used when the partial message redundancy mode is considered. © 2022 IEEE.

关键词： Redundancy

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Rolling Bearing Fault Diagnosis Based on Meta-Learning with Few-Shot Samples 3

Rolling Bearing Fault Diagnosis Based on Meta-Learning with ...

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3rd International Conference on Industrial Artificial Intelligence, IAI 2021

作者： He, Yunpeng Zang, Chuanzhi Zeng, Peng Wang, Mingxin Dong, Qingwei Liu, Yuqi Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang University of Technology Shenyang110870 China

ISBN: (纸本)9781665435178

As an essential component of mechanical equipment, the state of the rolling bearing has a substantial impact on the operation of the entire automatic system. The fault diagnostic technology based on deep learning surpasses the traditional fault diagnosis technology in many aspects and dramatically improves the accuracy of fault diagnosis but requires a massive amount of labeled data for training. Generally, it takes a lot of effort to obtain tagged data in a natural industrial environment. Therefore, this paper proposes a rolling bearing fault diagnosis method based on meta-learning, which applies the experience learned in the past to new tasks to use few-shot labeled rolling bearing fault samples for training to obtain reliable diagnosis accuracy. The results show that the proposed method can significantly improve few-shot rolling bearing fault samples' accuracy than other traditional methods. © 2021 IEEE.

关键词： Failure analysis

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