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Tracking Control in Presence of Obstacles and Uncertainties for Bioinspired Spherical Underwater Robots

Journal of Bionic engineering 2023年第1期20卷 323-337页

作者： Chunying Li Shuxiang Guo Jian Guo Graduate School of Engineering Kagawa UniversityTakamatsuKagawa761-0396Japan Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Information TechnologySchool of Life Science and TechnologyBeijing Institute of TechnologyBeijing100081China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems and Intelligent Robot Laboratory Tianjin University of TechnologyBinshui Xidao Extension 391Tianjin300384China

During marine missions,AUVs are susceptible to external disturbances,such as obstacles,ocean currents,etc.,which can easily cause mission failure or *** this paper,considering the strong nonlinearities,external disturbances and obstacles,the kinematic and dynamic model of bioinspired Spherical Underwater Robot(SUR)was ***,the waypoints-based trajectory tracking with obstacles and uncertainties was proposed for SUR to guarantee its safety and ***,the Lyapunov theory was adopted to verify the stability and the Slide Mode Control(SMC)method is used to verify the robustness of the control *** addition,a series of simulations were conducted to evaluate the effectiveness of proposed control *** tests,including path-following,static and moving obstacle avoidance were performed which verified the feasibility,robustness and effectiveness of the designed control ***,a series of experiments in real environment were performed to verify the performance of the control *** simulation and experimental results of the study supplied clues to the improvement of the path following capability and multi-obstacle avoidance of AUVs.

关键词： Path following Obstacle avoidance Bioinspired.Spherical underwater robot(SUR) Lyapunov theory

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Point cloud segmentation network based on multi-scale feature fusion

Point cloud segmentation network based on multi-scale featur...

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2024 China Automation Congress, CAC 2024

作者： Deng, Hao Cheng, Peng Cheng, Shengmei Liu, Cheng Du, Shaoyi Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Service School of Information Science and Technology Northwest University Xi’an China National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9798350368604

Aiming at improving the local feature extraction for point cloud learning, we introduce a point cloud segmentation network that enhances the PointNet++ framework with a local feature transformation module and a multi-scale feature fusion module. The local feature transformation module employs operations akin to convolution to restructure the point cloud's local feature dimensions, thus boosting the network's ability to extract detailed local features. Additionally, the multi-scale feature fusion module integrates semantic features of point clouds across various scales using a layered network design, aiming to heighten segmentation precision. Experimental results indicate that the proposed network can achieve a robust point cloud segmentation accuracy, reaching an accuracy rate of 85.8%. © 2024 IEEE.

关键词： Cloud platforms

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Research on Entity Recognition Method for Quality Standards of Concrete Prefabricated Components 1

Research on Entity Recognition Method for Quality Standards ...

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1st International Conference on intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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Low-light image enhancement and acceleration processing based on ZYNQ

Low-light image enhancement and acceleration processing base...

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2021 China Automation Congress, CAC 2021

作者： Chen, Zehe Wu, Shiqian Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan China

ISBN: (纸本)9781665426473

Most embedded vision systems are used to capture an image in low-light condition which results in an image with low visibility and quality. However, high-performance image enhancement algorithms take up lots of chip resources and are time consuming in embedded vision application scenarios with high real-time requirements. In this paper, a multi-core heterogeneous embedded vision system is designed based on ZYNQ, and it uses the optimized screen blending algorithm to complete the enhancement of low-light images. First, the inverse color coefficient is optimized to enhance the texture details of the image. Secondly, adaptive gamma correction is performed on the inverted image to maintain the trueness of the enhanced image color. Finally, through reasonable hardware and software co-design, the inverse color coefficient is calculated by hardware logic circuit to speed up the algorithm. Experimental results show that the low-light images collected and processed by this system have a certain improvement in the image quality than the original algorithm. The processing speed is nearly 5 times faster than without hardware acceleration, which meets the real-time requirements of embedded vision systems. © 2021 IEEE

关键词： Image enhancement

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Eye Movement Events Detection with KNN-GA and Prior Knowledge 2

Eye Movement Events Detection with KNN-GA and Prior Knowledg...

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2nd International Conference on Computer engineering and Application, ICCEA 2021

作者： Zhong, Zheng Fang, Hongping Zhang, Hanyuan Wu, Shiqian Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan China

ISBN: (纸本)9781665426169

Aiming at the problems of difficulty in threshold adjustment and low detection accuracy and efficiency, an eye movement event detection method combining K-Nearest Neighbor and genetic algorithm (KNN-GA) and prior knowledge is proposed. Firstly, design the absolute amplitude feature of eye movement to describe the eye movement event characteristics of PSOs, and then the KNN is used to pre-detect eye movement events based on the minimum feature subset generated by genetic algorithm;After that, screening rules based on prior knowledge are ted to further adjust and optimize the pre-detection results. Experimental results show that this algorithm avoids threshold adjustment, and its execution efficiency is equivalent to simple IVT and NH, meanwhile the detection accuracy of fixation, saccade, and PSOs are increased by at least 3.4%, 4.7%, and 12.7%, respectively, and the detection performance is robust under different noise levels. © 2021 IEEE.

关键词： Genetic algorithms

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Accurate Pose Tracking of Mobile Robot Using Entropy-based TrimICP in Dynamic Environment 48

Accurate Pose Tracking of Mobile Robot Using Entropy-based T...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Sun, Haodong Wang, Shuting Meng, Jie Liu, Yu Xie, Yuanlong Huazhong University of Science and Technology School of Mechanical Science and Engineering Wuhan China Guangdong Intelligent Robotics Institute Dongguan China Wuhan University of Technology Intelligent Transport System Research Center Wuhan China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

Pose tracking is one of the most critical techniques in mobile robot navigation, but dynamic environments will greatly reduce its accuracy and robustness. For addressing the above problem, this paper proposes a pose tracking method based on Monte Carlo localization and Entropy-based trimICP (E-trimICP) to achieve accurate and robust robot localization. First, a hybrid noise filtering method that fuses distance filtering and radius filtering is presented to improve the reliability of observation data. Then, an E-trimICP is designed to improve the accuracy of scan matching in dynamic environments. The proposed method can not only enhance the adaptability by determining the trim ratio based on the dynamic degree of the environment, but also improve the operational efficiency through the improved heap sorting. Ultimately, the effectiveness of the proposed pose tracking method is verified by real experiments using a self-developed mobile robot. © 2022 IEEE.

关键词： Mobile robots

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DOA Estimation via Adaptive Weighted Truncated Nuclear Norm

DOA Estimation via Adaptive Weighted Truncated Nuclear Norm

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第35届中国控制与决策会议

作者： Zhen Tian Qile Zhu Shun Fang Qi Wu Shiqian Wu Institute of Robotics and Intelligent System School of Information Science and Engineering Wuhan University of Science and Technology Software and Internet of things Engineering Jiangxi University of Finance and Economics

Aiming at the large error of traditional direction-ofarrival(DOA) estimation algorithm caused by the low signal-toratio and limited *** paper proposes a DOA estimation method based on adaptive weighted truncation nuclear ***,a new model which combines y norm and adaptive weighted nuclear norm is proposed to accurately reconstruct the received signal covariance matrix into a lowrank covariance matrix and a sparse covariance ***,the generalized alternating direction method of multipliers(GADMM) with an adaptive stopping criterion is used to solve this convex ***,based on the reconstructed low-rank noise-free covariance matrix,a multiple signal classification(MUSIC) algorithm is used to achieve DOA *** simulation results are provided to verify the effectiveness and superiority of the proposed method.

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Transformer based Online Continuous Multi-Target Tracking with State Regression 12

Transformer based Online Continuous Multi-Target Tracking wi...

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12th IEEE International Conference on Control, Automation and information sciences, ICCAIS 2023

作者： Wei, Xinwei Chen, Linxiu Zhang, Chenyu Lin, Yiru Zhang, Linao Liu, Xiaokai Jiang, Jixiang Yi, Wei University of Electronic Science and Technology of China School of Information and Communication Engineering China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China University of Electronic Science and Technology of China Research Institute of Electronic Science and Technology China

ISBN: (纸本)9798350328783

Multi-target Tracking (MTT) is the process of processing received measurements to maintain estimates of the current status of multiple targets, with important applications to autonomous driving, aerial reconnaissance, underwater operations, and others. In the model-based setting, Bayesian filtering can provide the theoretical optimal estimate in a single target scenario. However, in complex situations, uncertain factors such as changes in the number of targets will cause the amount of calculation to increase exponentially, resulting in a decline in tracking accuracy. To solve that problem, model-free methods based on deep-learning provide an attractive alternative, especially the state-of-the-art architecture Transformer based encoder-decoder prediction model, which outperforms the Bayesian filters in the single frame prediction tasks. However, when switching to continuous tracking, these algorithms need to be trained separately frame by frame to adapt to the new tasks. Still, there is no correlation between their predictions from different frames, which prevents them from fully utilizing all the measurements. In this paper, we propose an end-to-end Transformer based MTT method with state autoregression, which allows the model to have the capability of online continuous tracking and make total use of the entire trajectory. The results show that the proposed model is a great extension from single-frame prediction to online continuous tracking. © 2023 IEEE.

关键词： Forecasting

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User Perception and Evaluation of a Portrait Drawn by KUKA KR3 AGILUS Manipulator 9th

User Perception and Evaluation of a Portrait Drawn by KUKA K...

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9th International Conference on Interactive Collaborative robotics, ICR 2024

作者： Zagirov, Aidar Chebotareva, Elvira Osokin, Sergey Li, Hongbing Magid, Evgeni Intelligent Robotics Department Institute of Information Technology and Intelligent Systems Kazan Federal University 35 Kremlevskaya St. Kazan420008 Russia Department of Instrument Science and Engineering Shanghai Jiao Tong University Minhang Shanghai200240 China School of Electronic Engineering Tikhonov Moscow Institute of Electronics and Mathematics HSE University 34 Tallinskaya St. Moscow Russia

ISBN: (纸本)9783031713590

Creating an artistic portrait is considered one of the most complex areas of a generative art. Currently, generative artificial intelligence (AI) has made significant advancements in generating portraits of people based on photographic images. This has opened up new opportunities for utilizing AI achievements in robotic artistry. However, questions related to evaluation and human perception of such works, including their artistic value, remain open. In our work, we present experimental results of integrating the processing of human photographs using the generative model InstantID with a drawing robot and studying users’ reactions to the obtained images. In our experiments, we generated graphic portraits of people and constructed their images using KUKA KR3 AGILUS robot. We then asked participants to rate the resulting portraits based on three parameters: similarity/recognition, beauty/aesthetics, and evoked emotions. The results of the experiments can be used in social robotics, including art therapy that involves social robots and human-robot interaction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Social robots

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