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第40届中国控制会议

作者： Jiashun Dong Huaiyu Wu Zhihuan Chen Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

This paper designs an improved linear active disturbance rejection controller（LADRC） for a dual-joint robotic arm system. Firstly, the model of the dual-joint manipulator was established;Secondly, the manipulator control system based on traditional linear active disturbance rejection controller（LADRC） was designed, and the stability and rapidity of the linear active disturbance rejection control were analyzed. Then, in order to improve the tracking speed and the robustness of the system, an improved LADRC controller with proportional-derivative term（ILADRC PD） is designed for the first joint of the manipulator,and an improved LADRC controller with adaptive gain（ILADRC AG） is designed for the second joint of the ***, numerical simulation is presented via comparing the improved LADRC with the traditional method, which demonstrates that the controller proposed in this study has better performance.

关键词： LADRC dual-joint robotic arm system proportional-derivative item adaptive gain

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Two-loop Control of Harvesting Mechanical Arm base on Adaptive Input Shaping Algorithm

Two-loop Control of Harvesting Mechanical Arm base on Adapti...

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Virtual Reality, Human-Computer Interaction and Artificial Intelligence (VRHCIAI), International Conference on

作者： Mingming Sun Dexin Liu Basic Experimental Center for Natural Science University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China College of Robotics Beijing Union University Beijing China

When the harvesting mechanical arm clamps the tomato bunches and puts them into the picking basket, the acceleration and deceleration movement of the mechanical arm makes the tomato bunches swing in the moving process, which is easy to break the fruit stalks and cause them to fall off. First, in order to accurately track the desired position of the mechanical arm, the PID control was designed and implemented. Then the tomato stem is approximated as a flexible connecting rod with tomatoes as the load. Due to the different quality of the bunches picked each time, the system model changes with the load. The traditional input shaping algorithm could not suppress the vibration of tomato. Therefore, the adaptive input shaping control method is adopted to realize double closed-loop control and achieve the purpose of vibration suppression. The simulation results show that the algorithm has good suppression ability of end vibration and satisfactory control effect of manipulator displacement under variable load.

关键词： Vibrations Solid modeling Adaptation models PI control Uncertainty Heuristic algorithms Simulation

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Diabetic Retinopathy Lesion Segmentation Using Deep Multi-Scale Framework

SSRN

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SSRN 2023年

作者： Guo, Tianjiao Yang, Jie Yu, Qi Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong Univeristy Shanghai China Department of Ophthalmology Shanghai General Hospital Shanghai Jiao Tong University School of Medicine Shanghai China National Clinical Research Center for Eye Diseases Shanghai China

The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCNs) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCNs and refine the segmentation *** conducted our experiments on one local dataset and three public datasets including IDRiD, DDR, and DIARETDB1. The results of the area under the precision-recall-curve (AUPR) show that our approach achieves competitive *** improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Dynamic Movement Primitives based Parametric Gait Model for Lower Limb Exoskeleton

Dynamic Movement Primitives based Parametric Gait Model for ...

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第三十九届中国控制会议

作者： Wenhao Ma Rui Huang Qiming Chen Guangkui Song Cong Li Center for Robotics School of Automation Engineering University of Electronic Science and Technology of China (UESTC)

Lower limb exoskeleton robot is widely used in clinical rehabilitation training and daily walking for paraplegia patients. However, most of lower extremity exoskeleton products use fixed gait and speed, making them limited in use. Therefore,this paper proposes a parametric gait model for lower limb exoskeleton gait planning, which is based on the dynamic movement primitives method. By learning the characteristics of the sample curves of hip and knee joints at different velocities,the model can output the curve of hip and knee joints under continuous velocity variation. Thus, the lower limb exoskeleton is given an anthropomorphic variable gait which would enhance its dynamic performance and broaden the application scenarios.

关键词： lower limb exoskeleton gait planning parametric gait model

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Research on hybrid bounding box interference check algorithm for robot offline simulation system 2

Research on hybrid bounding box interference check algorithm...

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2021 2nd International Conference on Internet of Things, Artificial Intelligence and Mechanical automation, IoTAIMA 2021

作者： Li, Jing Zhong, Zhen Fan, Changqi Shen, Nanyan Lu, Yihao Feng, Zeyuan Dai, Zhenliang Yu, Hong Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Shanghai Mobile Internet Industry Promotion Center 306 Taopu Road Putuo District Shanghai200444 China Yanfeng Plastic Omnium Automotive Exterior Systems Company Limited No.540 Moyu Road Anting Shanghai200444 China

Aiming at the problem that the traditional industrial robot may interfere with the workpiece when running in the offline simulation environment, a hybrid bounding box algorithm based on octree is proposed. First, AABB is used for rough interference checks, which quickly eliminates the pairs of impossible objects to intersect, then OBB hierarchical bounding box is used for precise interference checks. A new OBB construction method is proposed, which recalculates the semi-axis length and centre of the constructed traditional OBB to make it surround the object as much as possible and reduce the space gap. A traversal method based on distance priority is proposed, which traversed the closest node every time by maintaining a priority queue. And in the update strategy, "update on demand"is adopted, which discards updating the entire tree before each intersection and only updates the current node during the intersection traversal to achieve the purpose of partial update. Experiments show that the proposed hybrid bounding box algorithm can detect interference faster and perform better real-time performance and accuracy. © Published under licence by IOP Publishing Ltd.

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Riemannian Optimistic Algorithms

arXiv

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arXiv 2023年

作者： Wang, Xi Yuan, Deming Hong, Yiguang Hu, Zihao Wang, Lei Shi, Guodong Australian Center for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2006 Australia Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Automation Nanjing University of Science and Technology Jiangsu210023 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China College of Computing Georgia Institute of Technology AtlantaGA30339 United States College of Control Science and Engineering Zhejiang University Zhejiang310058 China

In this paper, we consider Riemannian online convex optimization with dynamic regret. First, we propose two novel algorithms, namely the Riemannian Online Optimistic Gradient Descent (R-OOGD) and the Riemannian Adaptive Online Optimistic Gradient Descent (R-AOOGD), which combine the advantages of classical optimistic algorithms with the rich geometric properties of Riemannian manifolds. We analyze the dynamic regrets of the R-OOGD and R-AOOGD in terms of regularity of the sequence of cost functions and comparators. Next, we apply the R-OOGD to Riemannian zero-sum games, leading to the Riemannian Optimistic Gradient Descent Ascent algorithm (R-OGDA). We analyze the average iterate and best-iterate of the R-OGDA in seeking Nash equilibrium for a two-player, zero-sum, g-convex-concave games. We also prove the last-iterate convergence of the R-OGDA for g-strongly convex-strongly concave problems. Our theoretical analysis shows that all proposed algorithms achieve results in regret and convergence that match their counterparts in Euclidean spaces. Finally, we conduct several experiments to verify our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Bipartite Tracking Consensus for General Linear Multi-Agent Systems with Asynchronous Communications over Signed Networks

Bipartite Tracking Consensus for General Linear Multi-Agent ...

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第40届中国控制会议

作者： Runyao Chen Lulu Chen Jinliang Shao Wei Xing Zheng School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China School of Automation Engineering University of Electronic Science and Technology of China Research Center on Crowd Spectrum Intelligence Shenzhen Institute of Artifcial Intelligence and Robotics for Society School of Computing Engineering and Mathematics Western Sydney University

In this paper, the bipartite tracking consensus problem of general linear multi-agent systems over signed networks is investigated, in which an asynchronous communication manner is adopted. The asynchronous setting refers to that the clocks of each agent, at which the agent’s dynamics is updated, are independent of others. Relying on some related properties of the sub-stochastic matrix, the dynamics of the agents under the proposed asynchronous consensus protocol is rigorously *** is shown that the bipartite tracking consensus is implemented if the signed digraph contains a spanning tree. A simulation example is provided to verify our theoretical findings.

关键词： Bipartite tracking consensus Asynchronous communications General linear multi-agent systems Signed networks

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Cascade Graph Neural Networks for RGB-D Salient Object Detection 16th

Cascade Graph Neural Networks for RGB-D Salient Object Detec...

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16th European Conference on Computer Vision, ECCV 2020

作者： Luo, Ao Li, Xin Yang, Fan Jiao, Zhicheng Cheng, Hong Lyu, Siwei Center for Robotics School of Automation Engineering UESTC Chengdu China Abu Dhabi United Arab Emirates University of Pennsylvania Philadelphia United States University at Albany State University of New York Albany United States

ISBN: (纸本)9783030586096

In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information. A major technical challenge in performing salient object detection from RGB-D images is how to fully leverage the two complementary data sources. Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors. In this work, we introduce Cascade Graph Neural Networks (Cas-Gnn), a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection. Cas-Gnn processes the two data sources individually and employs a novel Cascade Graph Reasoning (CGR) module to learn powerful dense feature embeddings, from which the saliency map can be easily inferred. Contrast to the previous approaches, the explicitly modeling and reasoning of high-level relations between complementary data sources allows us to better overcome challenges such as occlusions and ambiguities. Extensive experiments demonstrate that Cas-Gnn achieves significantly better performance than all existing RGB-D SOD approaches on several widely-used benchmarks. Code is available at https://***/LA30/Cas-Gnn. © 2020, Springer Nature Switzerland AG.

关键词： Graph neural networks

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Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance

arXiv

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arXiv 2022年

作者： Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China

Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point’s movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate scene flow with ground truth for real-world data, this leaves no real-world dataset available to provide a large amount of data with ground truth for scene flow estimation. Therefore, many works use synthesized data to pre-train their network and real-world LiDAR data to finetune. Unlike the previous unsupervised learning of scene flow in point clouds, we propose to use odometry information to assist the unsupervised learning of scene flow and use real-world LiDAR data to train our network. Supervised odometry provides more accurate shared cost volume for scene flow. In addition, the proposed network has mask-weighted warp layers to get a more accurate predicted point cloud. The warp operation means applying an estimated pose transformation or scene flow to a source point cloud to obtain a predicted point cloud and is the key to refining scene flow from coarse to fine. When performing warp operations, the points in different states use different weights for the pose transformation and scene flow transformation. We classify the states of points as static, dynamic, and occluded, where the static masks are used to divide static and dynamic points, and the occlusion masks are used to divide occluded points. The mask-weighted warp layer indicates that static masks and occlusion masks are used as weights when performing warp operations. Our designs are proved to be effective in ablation experiments. The experiment results show the promising prospect of an odometry-assisted unsupervised learning method for 3D scene flow in real-world data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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3D Scene Flow Estimation on Pseudo-LiDAR: Bridging the Gap on Estimating Point Motion

arXiv

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arXiv 2022年

作者： Jiang, Chaokang Wang, Guangming Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

3D scene flow characterizes how the points at the current time flow to the next time in the 3D Euclidean space, which possesses the capacity to infer autonomously the non-rigid motion of all objects in the scene. The previous methods for estimating scene flow from images have limitations, which split the holistic nature of 3D scene flow by estimating optical flow and disparity separately. Learning 3D scene flow from point clouds also faces the difficulties of the gap between synthesized and real data and the sparsity of LiDAR point clouds. In this paper, the generated dense depth map is utilized to obtain explicit 3D coordinates, which achieves direct learning of 3D scene flow from 2D images. The stability of the predicted scene flow is improved by introducing the dense nature of 2D pixels into the 3D space. Outliers in the generated 3D point cloud are removed by statistical methods to weaken the impact of noisy points on the 3D scene flow estimation task. Disparity consistency loss is proposed to achieve more effective unsupervised learning of 3D scene flow. The proposed method of self-supervised learning of 3D scene flow on real-world images is compared with a variety of methods for learning on the synthesized dataset and learning on LiDAR point clouds. The comparisons of multiple scene flow metrics are shown to demonstrate the effectiveness and superiority of introducing pseudo-LiDAR point cloud to scene flow estimation. © 2022, CC BY.

关键词： Three dimensional computer graphics

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