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2023 China Automation Congress, CAC 2023

作者： Li, Xin Chen, Wenrui Wang, Yaonan Diao, Qiang Wu, Sijie Yang, Fan College of Electrical and Information Engineering Hunan University National Engineering Research Center for Robot Visual Perception and Control Changsha China School of Robotics National Engineering Research Center for Robot Visual Perception and Control Hunan University Changsha China College of Mechanical and Vehicle Engineering National Engineering Research Center for Robot Visual Perception and Control Hunan University Changsha China

ISBN: (纸本)9798350303759

This paper investigates a dexterous manipulation method for multi-fingered hands based on the approximate policy optimization (PPO) algorithm. First, a prior knowledge base of dexterous manipulation demonstration data is provided for the PPO algorithm, and the original model is pre-trained to guide fast network training. Second, a new reward function is proposed to optimize rotational manipulation action decisions based on hand joints, fingertip tactile, and object pose infor mation. Finally, a three-fingered hand is used as an example for rotational manipulation experiments, and the comparative simulation results show that the method is more efficient than the traditional PPO algorithm and provides ideas for dexterous manipulation of multi-fingered hands. © 2023 IEEE.

关键词： Reinforcement learning

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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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Posture-based Leader-follower Formation for Multiple Mobile robot Systems Using Data-based Adaptive Iterative Learning control

Posture-based Leader-follower Formation for Multiple Mobile ...

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2022 Chinese Automation Congress, CAC 2022

作者： Chaochen, Zhuolei Wang, Yaonan Tan, Haoran Zhou, Jiaming Lin, Qiong National Engineering Research Center for Robot Visual Perception and Control College of Electrical and Information Engineering Hunan University Changsha China

ISBN: (纸本)9781665465335

Multiple mobile robot systems (MMRSs) have great potential in various fields and their reliable application depends on accurate formation among robots. Focusing on the formation problem of MMRSs, a posture-based adaptive iterative learning control scheme (PB-AILC) is proposed in this paper. Firstly, the leader-follower formation model is built. Then PB-AILC scheme is introduced to optimize the control linear and angular velocity of follower robots at each iteration based on the robots' global posture only, guaranteeing that followers can steadily track their leader with prescribed relative distance and orientation. Meanwhile, we analyze its feasibility mathematically. Finally, numerical simulations illustrate the validity of the PB-AILC scheme and we compare PB-AILC with an iterative learning control (ILC) method to demonstrate its superiority. © 2022 IEEE.

关键词： Iterative methods

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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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Natural interactive control method for unmanned swarm systems based on gesture recognition

Natural interactive control method for unmanned swarm system...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Yijiang Wang, Yaonan Mo, Yang Zhu, Qing Miao, Zhiqiang Luo, Yuefan College of Electrical and Information Engineering National Engineering Research Center for Robot Visual Perception and Control Hunan University Changsha410082 China

ISBN: (纸本)9781665481090

Interactive control is very important for unmanned swarm systems to understand human intentions and execute tasks. However, traditional interactive control usually uses a keyboard, mouse, or remote controller, which is not intuitive and convenient enough. This paper proposes a natural interaction control method based on gesture recognition for unmanned swarm systems. First, considering the real-time requirement of interactive control, the YOLOv5 algorithm is improved by replacing the original GIoU loss function with the DIoU loss function to improve the performance of the algorithm. Then, according to the interactive control requirements of unmanned swarm systems, the corresponding gesture database is established. Finally, the AirSim platform is used to build the simulation system, and the interactive control based on gesture recognition is tested and verified. The experimental results show that the recognition accuracy of the control intention is 97% and the response time is approximately 0.027 s, which can realize the interactive control of unmanned swarm systems. © 2022 IEEE.

关键词： Mammals

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A Mobile robotic System for Data center Thermal Environment Measurement and Reconstruction

A Mobile Robotic System for Data Center Thermal Environment ...

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2021 China Automation Congress, CAC 2021

作者： Qin, Ronghan Fang, Qiu Wang, Yaonan The National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Hunan Changsha410082 China

ISBN: (纸本)9781665426473

Data centers are the key infrastructure for information services. The monitoring of the thermal environment of the data center computer room is an important work for its safe operation. This paper proposed a mobile robotic system for data center thermal environment measurement, which can freely sample the temperature and humidity data around the facilities within the computer room. On one hand, measuring the temperature and humidity in a mobile way can obtain far more environmental data than fixed-point sensors. On the other hand, the mobile robot can be used instead of manual inspection. In addition, a temperature field reconstruction method is provided based on the sampled temperature data. Theoretically, the temperature of locations that have not been measured can be evaluated by interpolating sample temperature around them. The reconstruction of the temperature field can present the variation of the temperature more clearly and help find the hot spots or locations with low cooling efficiency during the operation. Lastly, experiments are carried out to study the measurement error of the mobile robotic system and an error correction method is proposed. After that, the relationship between the temperature reconstruction error and the layout of sampling points is investigated. © 2021 IEEE

关键词： Interpolation

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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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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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Unsupervised Deep Homography Estimation based on Transformer 8

Unsupervised Deep Homography Estimation based on Transformer

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

作者： Jiang, Tianjian Fang, Qiu Zhu, Qing Wang, Yaonan Zhou, Zhen Chen, Lin Zhou, Jiaming Luo, Yuefan Wu, Chengzhong National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University Hunan Changsha410082 China Jiangxi Communication Terminal Industry Technology Research Institute China

ISBN: (纸本)9798350300178

Homography estimation is a crucial problem in computer vision, which aims to provide an optimal transformation matrix for aligning images captured from different viewpoints. Current methods extract shallow features from image pairs and introduce learnable mask modules to improve homography estimation performance. However, they struggle to capture long-term dependencies between features and comprehend the global structures of image features. A deep unsupervised homography learning framework is proposed in this paper, consisting of a weight-sharing feature extraction network and a homography estimation network based on the Transformer model. The former extracts the local features of images, while the latter learns the correlation between them and understands the global features of images, enabling the algorithm to better estimate the homography of unaligned images. Experimental results demonstrate that the proposed method outperforms the advanced methods for estimating homography matrices in the CA-Unsupervised dataset. © 2023 IEEE.

关键词： Computer vision

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