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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Wang, Sifei Miao, Zhiqiang Lin, Qiong Lin, Jie Zeng, Jianxin Chen, Lin Wang, Yaonan Hunan University College of Electrical and Information Engineering The National Engineering Laboratory for Robot Visual Perception and Control Changsha410082 China

ISBN: (纸本)9798350327182

Trajectory tracking is a basic problem in quadrotor control. To achieve a better control effect, MPC is introduced in the control of quadrotors. In this paper, we introduce the error-state dynamic model of a quadrotor firstly. Then, we develop an MPC method via the Lie group of special euclidean group based on the error-state dynamic model of a quadrotor. The proposed method is established on a manifold, thus the smoothness of the quadrotor trackjectory can be guaranteed. Our scheme is developed in error-state, which means that we take the velocity of the target trajectory into consideration for the dynamic model building. Therefore the cost function of our proposed approach is convex while the traditional MPC method is not when tracking a nonlinear trajectory. At the end of this paper, we compare our proposed MPC method with the traditional MPC method in one hundred sets of simulations. In our proposed method, the errors of position and orientation converge faster than the errors in the traditional method. The simulation results show that our proposed method is superior to traditional MPC scheme while tracking a nonlinear trajectory with a random initial position and verity the correctness of our MPC method. © 2023 IEEE.

关键词： Model predictive control

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Dynamic Movement Primitives Based robot Skills Learning

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Machine Intelligence Research 2023年第3期20卷 396-407页

作者： Ling-Huan Kong Wei He Wen-Shi Chen Hui Zhang Yao-Nan Wang School of Intelligence Science and Technology University of Science and Technology BeijingBeijing 100083China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China School of Robotics and the National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint *** DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion *** addition,motions are categorized into different goals and *** is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning *** experiment test on the Baxter robot verifies the effectiveness of the proposed method.

关键词： Dynamic movement primitives(DMPs) trajectory tracking control robot learning from demonstrations neural networks(NNs) adaptive control

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A decomposition-based multiobjective evolutionary algorithm for sparse reconstruction 9th

A decomposition-based multiobjective evolutionary algorithm ...

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9th International Conference on Swarm Intelligence, ICSI 2018

作者： Zhu, Jiang Cai, Muyao Tian, Shujuan Xu, Yanbing Pei, Tingrui College of Information Engineering Xiangtan University Xiangtan411105 China National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

ISBN: (纸本)9783319938141

Sparse reconstruction is an important method aiming at obtaining an approximation to an original signal from observed data. It can be deemed as a multiobjective optimization problem for the sparsity and the observational error terms, which are considered as two conflicting objectives in evolutionary algorithm. In this paper, a novel decomposition based multiobjective evolutionary algorithm is proposed to optimize the two objectives and reconstruct the original signal more exactly. In our algorithm, a sparse constraint specific differential evolution is designed to guarantee that the solution remains sparse in the next generation. In addition, a neighborhood-based local search approach is proposed to obtain better solutions and improve the speed of convergence. Therefore, a set of solutions is obtained efficiently and is able to closely approximate the original signal. © Springer International Publishing AG, part of Springer Nature 2018.

关键词： Multiobjective optimization

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Motion Planning for Mobile robots with Temporal Logic Specifications 8

Motion Planning for Mobile Robots with Temporal Logic Specif...

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8th IEEE International Conference on Advanced robotics and Mechatronics, ICARM 2023

作者： Zeng, Jianxin Wang, Yaonan Miao, Zhiqiang Wang, Sifei College of Electrical and Information Engineering The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

ISBN: (纸本)9798350300178

In this paper, we propose a motion planning method for mobile robots in order to satisfy task requirements specified in linear temporal logic (LTL). The proposed method follows the traditional hierarchical planning workflow, including path planning and trajectory planning. Firstly, we propose a path planning method based on rapidly exploring random trees (RRT*) and Dubins curve. The tree derived from RRT∗ is expanded with the help of Buchi automaton converted by an LTL formula. The proposed method can automatically adjust the iterations of sampling according to the scale of Buchi automata. Then, we formulate an optimization problem for the generation of trajectory. We use Bezier curve to express the trajectory and use differential flatness of mobile robots to simplify the optimization problem. Finally, we validate the practicality of our method through simulations and real-world experiments. © 2023 IEEE.

关键词： Mobile robots

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OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6225-6239页

作者： Teng, Fei Zhang, Jiaming Peng, Kunyu Wang, Yaonan Stiefelhagen, Rainer Yang, Kailun Hunan University School of Robotics National Engineering Research Center of Robot Visual Perception and Control Technology Changsha410082 China Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Karlsruhe76131 Germany Institute for Visual Computing ETH Zurich Zurich8092 Switzerland

Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM). This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (∼1\rm GFlops). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of 84.93% in mIoU on the UrbanLF-Real Extended dataset, with a gain of +3.69%. The source code for OAFuser is available at https://***/FeiBryantkit/OAFuser. © 2020 IEEE.

关键词： Semantic Segmentation

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Adaptive extended state observer-based distributed fault detection and estimation for an unstable wave equation 36

Adaptive extended state observer-based distributed fault det...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Feng, Yun Wang, Yaonan Kang, Wen Miao, Zhiqiang Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha China University of Science and Technology School of Automation and Electrical Engineering Beijing China

ISBN: (纸本)9781665437127

In this paper, we study the fault detection along with the estimation problem for an unstable wave equation based on an adaptive extended state observer (ESO) using only boundary measurements. A fault detection filter (FDF) is proposed in the first place to detect fault based on a naive observer. Once an alarm has been raised by the FDF, a fault estimation filter is activated for fault identification by incorporating an adaptive law into the naive observer. The fault estimator along with the adaptive state observer consists of an adaptive ESO motivated by the intuitive idea of ESO. Numerical simulations on a faulty wave equation are presented to validate the fault detection and estimation performance. © 2021 IEEE.

关键词： Wave equations

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Double-layer Descriptors and High-dimensional Search Mechanisms for Partial Point Cloud Registration

Double-layer Descriptors and High-dimensional Search Mechani...

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2023 China Automation Congress, CAC 2023

作者： Wang, Zhiyu Fang, Qiu Wang, Yaonan Lu, Hong Ye, Jun National Engineering Research Center for Robot Visual Perception and Control Technology School of Electrical and Information Engineering Hunan University Hunan Changsha410082 China

ISBN: (纸本)9798350303759

Partial point cloud registration is an essential preprocessing technique to generate complete 3D shapes that aim to transform partial scans into a common coordinate system. Existing methods that utilize geometric alignment rarely combine local and global information, which results in many incorrect correspondences. In the paper, a two-layer descriptor and a high-dimension searching approach (DDHSM) are utilized to extract and select the correct correspondences. Firstly, in the first layer, the Fast Point Feature Histogram (FPFH) is used to describe the feature of every point with high-dimension information based on its robustness and fastness. Then a high-dimensional search is considered to extract the initial corresponding set. In the second layer, global information is introduced to find the correct correspondences. For each point pair in the initial corresponding set, the normal angles are compared for all remaining points that satisfy the distance requirement. Finally, the singular value decomposition (SVD) method is implemented to compute the rigid transformation based on the updated corresponding set. Experimental results and comparisons with state-of-the-art methods demonstrate the effectiveness and feasibility of our approach. © 2023 IEEE.

关键词： Graphic methods

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Self-Contrastive Learning Fault Diagnosis Network for Signal Imaging: A Case Study

Self-Contrastive Learning Fault Diagnosis Network for Signal...

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2024 China Automation Congress, CAC 2024

作者： He, Wenbin Mao, Jianxu Wang, Yaonan Li, Zhe Yan, Feng Liu, Kexin College of Electrical & Information Engineering National Engineering Research Center of Robot Visual Perception & Control Technology Hunan University Hunan China College of Control Science & Engineering State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang China

ISBN: (纸本)9798350368604

The deep convolution method based on MSDP signal imaging has been proven to be an effective means of monitoring the robot grinding process. This method has very high requirements on the quality of imaging and requires a lot of computing resources. This paper proposes an image self-contrastive learning network that can effectively extract fault information from low-pixel fuzzy images and achieve efficient and accurate condition monitoring. The network first calculates the correlation distance matrix between two identical modified symmetrized dot pattern (MSDP) images and uses the generated similarity labels to calculate the contrast loss to extract the embedded features of the image. Finally, the classification network is trained using the cross entropy loss to achieve the classification of fault images. The method was verified on a robotic grinding platform to significantly reduce the calculation time while maintaining the recognition accuracy, which provides reliable technical support for the real-time monitoring of the robotic grinding process. © 2024 IEEE.

关键词： Contrastive Learning fault diagnosis robotic grinding signal imaging

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An Intelligent Route Recommendation Method for Smart Internet-Connected Electric Vehicles Under the Coupling of Traffic and Electricity 42

An Intelligent Route Recommendation Method for Smart Interne...

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42nd Chinese control Conference, CCC 2023

作者： Xie, Jinhuan Liu, Dongqi Yang, Zhenwu School of Electrical and Information Engineering Changsha University of Science and Technology Hunan 410114 China National Engineering Research Center for Robot Visual Perception and Control Technology Hunan 410012 China

ISBN: (纸本)9789887581543

Aiming at the charging and navigation strategy of electric vehicles in the road-electricity coupling scenario, this paper proposes a hybrid planning travel scheme based on the pre-charging and charging warning model based on the road traffic congestion predicted by the big data of the Internet of Vehicles. This model integrally balances the interests of users and power grid in the coupled road-electric network, and solves the optimal path scheme under different travel scenarios based on collaborative filtering algorithm and user feedback, considering the influence of vehicle departure time and remaining power in short time scale. It is experimentally demonstrated that the proposed route hybrid recommendation model has better planning effect compared with the single travel model. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Roads and streets

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MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model

arXiv

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arXiv 2025年

作者： Liu, Jian Sun, Wei Yang, Hui Zheng, Jin Geng, Zichen Rahmani, Hossein Mian, Ajmal National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering China School of Robotics Hunan University Changsha410082 China Central South University Changsha410082 China Lancaster University LA1 4YW United Kingdom The University of Western Australia WA6009 Australia

Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, current methods rely on shape priors or CAD models of the intra-class known objects. We propose a diffusion-based monocular category-level 9D object pose generation method, MonoDiff9D. Our motivation is to leverage the probabilistic nature of diffusion models to alleviate the need for shape priors, CAD models, or depth sensors for intra-class unknown object pose estimation. We first estimate coarse depth via DINOv2 from the monocular image in a zero-shot manner and convert it into a point cloud. We then fuse the global features of the point cloud with the input image and use the fused features along with the encoded time step to condition MonoDiff9D. Finally, we design a transformer-based denoiser to recover the object pose from Gaussian noise. Extensive experiments on two popular benchmark datasets show that MonoDiff9D achieves state-of-the-art monocular category-level 9D object pose estimation accuracy without the need for shape priors or CAD models at any stage. Our code will be made public at https://***/CNJianLiu/MonoDiff9D. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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