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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Liu, Tailai Miao, Zhiqiang Qin, Guangyu Wu, Wenjie Li, Lin Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China

ISBN: (纸本)9798350372601

This paper deals with the problem of combining task and motion planning for a manipulator system. We propose a linear temporal logic (LTL) approach which follows the traditional hierarchical planning process, including task planning and motion planning. Firstly, the feasible network topology graph is obtained from the high-level task planning module, and the planning algorithm of search is used to obtain the task sequence. Secondly, the coordination module receives the task sequence to extract the manipulator's action sequence to make planning suggestions. Furthermore, a lower level planning module is used to receive the suggestions and motion planning is performed using sampled planning algorithms to generate and output the motion trajectory. Finally, we verified the feasibility of our method through simulations and physical experiments. Our method has smoother trajectories and is about 10% more efficient than the traditional method of setting way points. © 2024 IEEE.

关键词： Motion planning

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Velocity-free Formation control for Quadrotors Using High-order Sliding Mode Differentiator

Velocity-free Formation Control for Quadrotors Using High-or...

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

作者： Lin, Qiong Miao, Zhiqiang Wang, Yaonan Hunan University College of Electrical and Information Engineering Changsha410082 China National Engineering Research Center for Robot Visual Perception and Control Changsha China

ISBN: (纸本)9798350327182

This paper deals with the problem of achieving formation control for underactuated multiple quadrotors without velocity measurements by employing the higher-order sliding mode (HOSM) differentiator. The primary objective of this work is to attain a coordinated formation behavior among the quadrotor group. In this paper, the dynamics of each quadrotor are transformed into a new form, wherein the attitude is described using rotation matrices. Subsequently, an intermediate guide is designed using the HOSM differentiator, taking into account the desired relative positions between the quadrotors and their actual positions. Next, the actual control thrusts and torques for each quadrotor are designed and solved by the intermediate guide. Finally, rigorous theoretical analysis and multiple simulations are performed to ensure that the multiple quadrotors converge asymptotically and accurately to the desired formation shape. © 2023 IEEE.

关键词： Sliding mode 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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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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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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HNSRRT∗:A Path Planning Algorithm Based On Heuristic Non-Uniform Sampling Method In Complex Obstacle Environment 42

HNSRRT∗:A Path Planning Algorithm Based On Heuristic Non-Un...

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42nd Chinese control Conference, CCC 2023

作者： Xu, Zhiwen Zhang, Hui Chen, Bo Zhou, Xidong Yin, Songtao Yang, Lian College of Electrical and Information Engineering Changsha University of Science and Technology Hunan China College of Robotics Robot Visual Perception and Control Technology National Engineering Research Center Hunan University Hunan China

ISBN: (纸本)9789887581543

The traditional sampling-based algorithm such as Rapidly Random-exploring Tree (RRT) and various varieties have achieved tremendous success in the area of path planning. However, their excessive exploration in the state space leads to long time to find the optimal solution, large memory usage and cannot guarantee the quality of the planned path(generally evaluated by the cost of search time and the length of path) in sophisticated space. In this article, we propose an optimal path planning algorithm based on heuristic non-uniform sampling, namely the HNSRRT∗, which successfully plans path in complex obstacle environments with optimal length and minimum time cost. The HNSRRT∗ utilizes heuristic function to generate non-uniform sampling distribution by Gaussian distribution,and constraints on sampling points can reduce the time wasted and path length increase caused by excessive exploration. We test the proposed HNSRRT∗ in 2D and 3D complex obstacle environment,comparing it with the three traditional sampling-base algorithms. The simulation results indicated that the effectiveness and efficiency of HNSRRT∗ and have an obvious improvement in term of time cost, path length compared with the existing algorithms. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Motion planning

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Tightly-Coupled LiDAR-visual SLAM Based on Geometric Features for Mobile Agents

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Feature...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Cao, Ke Liu, Ruiping Wang, Ze Peng, Kunyu Zhang, Jiaming Zheng, Junwei Teng, Zhifeng Yang, Kailun Stiefelhagen, Rainer Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics Germany Hunan University School of Robotics China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology China

ISBN: (纸本)9798350325706

The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to dynamic and challenging situations, such as poor lighting conditions and motion blur. To tackle this issue, we propose a tightly-coupled LiDAR-visual SLAM based on geometric features, which includes two sub-systems (LiDAR and monocular visual SLAM) and a fusion framework. The fusion framework associates the depth and semantics of the multi-modal geometric features to complement the visual line landmarks and to add direction optimization in Bundle Adjustment (BA). This further constrains visual odometry. On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features. It adjusts the direction of linear feature points and filters out outliers, leading to a higher accurate odometry system. Finally, we employ a module to detect the subsystem's operation, providing the LiDAR subsystem's output as a complementary trajectory to our system while visual subsystem tracking fails. The evaluation results on the public dataset M2DGR, gathered from ground robots across various indoor and outdoor scenarios, show that our system achieves more accurate and robust pose estimation compared to current state-of-the-art multi-modal methods. © 2023 IEEE.

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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1458-1475页

作者： Jing Liang Hongyu Lin Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001 School of Electrical Engineering and Automation Henan Institute of TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China KINDI Center for Computing Research Qatar UniversityDohaQatarand also with the Nanyang Technology UniversitySingapore College of Electrical and Information Engineering and also with the National Engineering Research Center for Robot Visual Perception and Control TechnologyHunan UniversityChangsha 410082China IEEE

In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective *** paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision *** to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal *** proposed algorithm adopts a dual-population framework and an improved environmental selection *** utilizes a convergence archive to help the first population improve the quality of *** improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first *** combination of these two strategies helps to effectively balance and enhance conver-gence and diversity *** addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is *** proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions.

关键词： Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.

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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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Improved YOLOv7 Based on Transformer for Object Detection in UAV-Captured Images

Improved YOLOv7 Based on Transformer for Object Detection in...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Luo, Yuefan Zhu, Qing Zhou, Zhen Chen, Lin Zhou, Jiaming Jiang, Tianjian Li, Yijiang Wang, Danwei Wang, Yaonan Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China School of Electrical and Electrical Engineering Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798350337020

As the drone captures image targets at different flying altitudes, their scales may vary significantly, which can pose challenges for the object detection model to accurately detect them. Additionally, tiny objects in the image contain minimal information, making them difficult to distinguish from the background. To overcome these two challenges, we proposed a network architecture that aims to improve the accuracy of tiny object detection in drone images. Specially, we designed a tiny object detector(TOD) that can effectively extract features of tiny objects and distinguish between tiny object features and image background. Furthermore, this TOD module contains a Convolutional visual Attention Network (CVAN) to better focus on the regions of tiny objects. Experimental results demonstrate that the proposed method achieves mAP@.5 accuracy of 53.9% on the VisDrone2021-test-dev dataset and improves by 2.8 % compared to YOLOv7. © 2023 IEEE.

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