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Efficient path planning for a dexterous arm–hand in complex environments

robotics and Autonomous Systems 2025年 193卷

作者： Wenpei Fan Yaonan Wang Wenrui Chen Licheng Liu Conghui Tang Xin Li Mingjie Dong The College of Electrical and Information Engineering Hunan University Lushan South Road Changsha 410082 Hunan China The National Engineering Research Center for Robot Visual Perception and Control Lushan South Road Changsha 410082 Hunan China Beijing University of Technology Chaoyang District Beijing 100124 China

Path planning represents a critical research direction for dexterous arm–hand (DAH) systems. However, path planning for high-degree-of-freedom manipulators presents the following challenges: (1) time-consuming collision detection, and (2) an expanded search space due to high-dimensional configurations, particularly in dynamic environments. In this paper, a new path planning strategy based on rapidly-exploring random tree (RRT) path is proposed for the DAH. Firstly, an adaptive step-size RRT (ADA-RRT*) algorithm is proposed to avoid the tunneling problem caused by discrete collision detection. Secondly, to improve the efficiency of the algorithm in high-dimensional spaces, a hierarchical planning framework is first introduced, consisting of coarse planning and fine planning. Coarse planning quickly finds a rough path with large steps without considering the tunneling problem, which then guides the fine planning. Then, the beetle antennae optimization algorithm and multi-objective optimization algorithm are used to optimize the global path, reducing path length and improving path safety. Finally, the execution of corresponding simulations and experiments demonstrates the effectiveness and efficiency of the proposed method.

关键词： Motion planning

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Online Parameter Estimation For Uncertain robot Manipulators With Fixed-time Convergence

Online Parameter Estimation For Uncertain Robot Manipulators...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chengzhi Zhu Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.

关键词： Parameter estimation Filtering Heuristic algorithms Estimation Real-time systems Manipulator dynamics Convergence

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Stiffness Estimation and Intention Detection for Human-robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation robot sensing systems Manipulators robots

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Towards to robust and generalized medical image segmentation framework

arXiv

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Liu, Lizhu Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University Vector Institute TorontoM5G 1M1 Canada

—To mitigate the radiologist’s workload, computer-aided diagnosis with the capability to review and analyze medical images is gradually deployed. Deep learning-based region of interest segmentation is among the most exciting use cases. However, this paradigm is restricted in real-world clinical applications due to the poor robustness and generalization. The issue is more sinister with a lack of training data. In this paper, we address the challenge from the representation learning point of view. We investigate that the collapsed representations, as one of the main reasons which caused poor robustness and generalization, could be avoided through transfer learning. Therefore, we propose a novel two-stage framework for robust generalized segmentation. In particular, an unsupervised Tile-wise AutoEncoder (T-AE) pretraining architecture is coined to learn meaningful representation. Furthermore, this learned prior knowledge is transferred to the segmentation benchmark. Coupled with an image reconstruction network, the representation keeps to be decoded, encouraging the model to capture more semantic features. Experiments of lung segmentation on multi chest X-ray datasets are conducted. Empirically, the comprehensive results demonstrate the superior generalization capability of the proposed framework on unseen domains in terms of high performance and robustness to corruption, especially under the scenario of the limited training data. © 2021, CC BY.

关键词： Computer aided diagnosis

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Dynamic Update Siamese Networks with Deeper Features

Dynamic Update Siamese Networks with Deeper Features

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作者： Jianhao Tan Shanshan Tan Wang Yin Liming Liu Siyuan Zhang Yaonan Wang College of Electrical and Information Engineering Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology

visual object tracking has been a concern topic these years,and many trackers have achieved good results in various *** researches and breakthroughs have made many improvements to solve problems such as drift,lighting,deformation and *** this paper,we improve the structure of the AlexNet[1] network by designing the three important influencing factors of the receptive field size,total network step size,and feature filling of the twin *** from this,we add a smoothing matrices and a background suppression matrices to effectively learn the features of the first few frames as much as *** multilayer feature elements can learn online about target appearance changes and background suppression,and we train them by using continuous video sequences.

关键词： Target tracking Siamese network Residual network Dynamic update

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Machine learning–based robust trajectory tracking control for FSGR

Machine learning–based robust trajectory tracking control f...

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7th International Symposium on Test Automation and Instrumentation, ISTAI 2018

作者： Jia, Lin Wang, Yaonan Zhang, Changfan Zhao, Kaihui Zhou, Langming College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China College of Electrical and Information Engineering Hunan University of technology Zhuzhou China

Here, a robust adaptive trajectory tracking algorithm is proposed for free-form surface grinding robot (FSGR) in metal surface production line. Machine-learning method is used for robot dynamic approximation which is hard to obtain directly. Adaptive law is proposed to adjust the neural network parameters. Sliding-mode control is employed to deal with the disturbance, joint friction, and approximation error of the adaptive machine learning. The scheme based on machine-learning feedforward compensation can significantly reduce the chattering of sliding mode. The performance of the proposed control scheme is illustrated through simulations. © 2019 Institution of engineering and technology. All rights reserved.

关键词： Machine learning

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Cleaning robot Operation Decision Based on Causal Reasoning and Attribute Learning*

Cleaning Robot Operation Decision Based on Causal Reasoning ...

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2020 IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yapeng Li Dongbo Zhang Feng Yin Ying Zhang Institute of Automation and Electronic Information Xiangtan University Xiangtan China Institute of National Engineering Laboratory of Robot Vision Perception and Control Technology Xiangtan University Xiangtan China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

In order to improve the operation ability of cleaning robots, this paper proposes a decision method for cleaning robot's operation mode. Firstly, we use the hierarchical expression ability of deep network to obtain the attributes of garbage such as state, shape, distribution, size and so on. Then the causal relationship between the attributes and the operation modes can be built by using joint learning of association attributes with depth network model and causal inference. Based on this, a fuzzy inference decision network is designed. With the help of causal analysis, the structure of the decision model is greatly simplified. Compared with conventional fuzzy neural networks, the total parameters of the model are reduced by 2 / 3. The method proposed in this paper imitates the way that human dispose of different types of garbage and has good interpretability. The experimental results verify the effectiveness of the proposed method.

关键词： Training Shape Semantics Fuzzy neural networks Cleaning Cognition robots

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A fast and robust visual detection method with binary template matching for glass bottle body

A fast and robust visual detection method with binary templa...

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第三十八届中国控制会议

作者： Xianen Zhou Yaonan Wang Qing Zhu The College of Electrical and Information Engineering Hunan University The National Engineering Laboratory for Robot Visual Perception and Control Technology

As multiple glass bottle bodies appear in the same one image from a view, the high precision and speed localization of the bottle body is difficult for the traditional visual detection methods. To overcome the problem, the imaging system is first briefly presented. And a new visual detection framework, named BTMfast, for glass bottle body with binary template matching is proposed. First, the input image is down-sampled to reduce the computational complexity. The subimages containing body region are obtained according to the position priors. Then, the neck or body of a bottle is taken as the template, and the template image and each sumimage is segmented with OTSU segmentation algorithm. Thirdly, the central line of the body in the original image can be obtained by the binary template matching. Finally, the top boundary of the bottle mouth is enhanced by a filtering operation with a new kernel. And the mouth position is obtained by segmenting the filtered image and column scanning. Two datasets are created for evaluating the performance of the proposed method. Compared with many conventional approaches on the two datasets, our method can achieve the optimal result. The localization error is about 1.5, the consumed time is about 4.3 ms.

关键词： visual detection template matching localization glass bottle body

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Target Tracking Based on Correlation Filter for Scale Offset

Target Tracking Based on Correlation Filter for Scale Offset

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作者： Jianhao Tan Xiaoping Ma Yaonan Wang College of Electrical and Information Engineering Hunan University National Engineering Laboratory for Robot Visual Perception and Control Technology

Existing scale estimation methods mainly use fixed-size tracking box for target ***,when the moving direction of the target changes from near to far or from far to near,the size of the current tracking box cannot adapt to the scale *** order to overcome the disadvantage of invariant size of tracking box in the scale change,this paper proposes a scale offset estimation method based on correlation filter by combining the state of previous frame and current frame of the *** the scale proposal box in the scale layer,and adjust the position and size of the actual tracking box in real time according to the size of the proposal box,target position and scale *** this paper,OTB-100 is used as the *** obtained tracking results show that our algorithm Ours has better tracking performance than the existing others tracking algorithm in scale variation,and it has better tracking effect and stronger robustness in drift and occlusion events.

关键词： frame Target Tracking Based on Correlation Filter for Scale Offset Filter

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Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

arXiv

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

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