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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Qiguang Lin Chaojie Yan Qiang Li Yonggen Ling Yu Zheng Wangwei Lee Zhaoliang Wan Bidan Huang Xiaofeng Liu Jiangsu Key laboratory of Special Robotic Technology College of IoT Engineering Hohai University Changzhou Jiangsu P.R. China State Key Laboratory of Industrial Control and Technology Zhejiang University Institute of Cyber-System and Control Hangzhou P.R. China Neuroinformatics Group Center for Cognitive Interaction Technology (CITEC) Bielefeld University Bielefeld Germany Tencent Robotics X Shenzhen China School of Computer Science and Engineering Sun Yat-sen University Guangzhou P.R.China

In this paper, we present a new approach to estimate the pose of an object being manipulated by a multi-fingered robotic hand. The method utilizes advanced tactile sensors with high spatial resolution to optimize the estimation of the object's pose using an Extended Kalman Filter (EKF) based approach. We defined and derived the state and measurement equations, as well as evaluated the estimation accuracy in grasping tasks. The approach is able to effectively account for the pose transition caused by tactile pushing, and the mapping from the object's pose to the contact position and normal direction as measured by the tactile sensor. The method was evaluated in multiple grasping experiments in simulation scenarios. Results show that the estimation can converge towards the ground truth in a relatively short period of time, with displacement and rotation errors remaining within acceptable levels. This new method has the potential to improve the accuracy and reliability of robotic grasping and manipulation tasks.

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Point Cloud based Motion State Estimation Method for Autonomous Driving

Point Cloud based Motion State Estimation Method for Autonom...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Tao Jiang Enzhi Zhu Sidong Wu Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Xianggui Tang School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

Generally, motion vector information primarily stems from moving objects, while static objects contribute minimally to the estimation task. Traditional approaches for motion vector estimation typically rely on scene flow methods that depend on deep models to extract features from individual points at a high cost. These methods then acquire flow information through complex matching mechanisms or feature decoding. Such approaches are computationally expensive and exhibit substantial latency. Moreover, they neglect the importance of motion objects in motion vector estimation and the interference from static objects. Therefore, this paper introduces a novel method that first performs point cloud motion segmentation and subsequently estimates motion vectors. This approach focuses on leveraging point cloud information annotated with motion objects to estimate three-dimensional scene flow more effectively. By employing motion segmentation, we can obtain annotations for moving objects, enabling greater emphasis on the estimation of motion vectors for more challenging cases. In our experiments conducted on the KITTI dataset, the proposed method demonstrates superior performance compared to existing scene flow estimation methods. Specifically, without considering motion segmentation errors, the error in the motion direction is only 0.0363 m/s, showcasing better performance. Additionally, our method achieves an error of 0.076 m for three-dimensional endpoint error (EPE3D), showcasing distinct advantages over current scene flow networks.

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LPL-SLAM: LiDAR SLAM with Plane and Line Optimization for Structural Environment

LPL-SLAM: LiDAR SLAM with Plane and Line Optimization for St...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Huanfeng Zhao Meibao Yao Xueming Xiao Bo Zheng Tao Cao Tao Hu Intelligent Robotics Lab (IRL) School of Artificial Intelligence Jilin University Changchun China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education China CVIR Lab Changchun University of Science and Technology Changchun China Key Lab of Opto-Electronic Measurement and Optical Information Transmission Technology Shanghai Aerospace Control Technology Institute Shanghai China

In LiDAR SLAM, data association of sparse LiDAR scan and high computational complexity of point-to-model metrics limit real-time applications of joint optimization module like Bundle Adjustment in Visual SLAM, which leads to cumulative errors deteriorating performance. To address this drawback, in this paper, we propose LPL-SLAM with plane and line optimization for the structural environment. Since plane and line primitives are ubiquitous in man-made environments, we treat the primitives as landmarks to process scans. We introduce line and plane factors relying on line-to-line and plane-to-plane metrics to optimize keyframe poses, planes and lines. With the line and plane factors, our system avoids a large-scale optimization problem and yields accurate and consistent tracking poses. Experimental results demonstrate that LPL-SLAM outperforms the state-of-the-art algorithms and achieves real-time performance.

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Unsupervised Video Anomaly Detection with Self-Attention Based Feature Aggregating

Unsupervised Video Anomaly Detection with Self-Attention Bas...

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International Conference on Intelligent Transportation

作者： Zhenhao Ye Yanlong Li Zhichao Cui Yuehu Liu Li Li Le Wang Chi Zhang National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China School of Software Engineering Xi'an Jiaotong University Xi'an Shaanxi China School of Electronics and Control Engineering Chang'an University Department of Automation BNRist Tsinghua University Beijing China

Anomaly detection in surveillance videos is a crucial and challenging task in the intelligent transportation systems. Previous methods utilize a memory module to store prototypical feature embeddings as normal patterns learned from normal training data. However, due to the complexity of real-world scenarios, it is difficult to choose an appropriate size of memory module which can not only memorize normal patterns comprehensively, but also capable of dealing with unseen normal scenarios. To tackle this problem, we learn normal video patterns by constructing and exploring correlations between visual semantics. In the training stage, we act the self-attention map between embeddings as a description of information association between different visual semantics. A self-attention based feature aggregating module is designed to regenerate a feature map through aggregating embeddings with similar information based on the attention map. By decoding the generated feature map instead of the original one to predict the future frame of the input video clip, the model learns to build strong information association between normal visual semantics. Moreover, we observe that abnormal embeddings hardly build strong association with others. Thus, we design a feature-level anomaly criterion referred as prior deviation to increase the difference between attention maps generated by normal and abnormal frames. In the inferring stage, the proposed prior deviation jointly detects anomalies with pixel-level frame prediction error. Experiment results and ablation studies on mainstream benchmarks demonstrate the effectiveness of our design.

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When Shannon Meets Nonstochastic Information: Communication and Estimation

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IEEE Transactions on Automatic control 2025年

作者： Zhang, Wentao Zuo, Zhiqiang Zhao, Rui Wang, Yijing Hu, Guoqiang Zhang, Hui Wang, Yaonan Hunan University School of Robotics Changsha410082 China Nanyang Technological University Continental-NTU Corporate Lab Singapore639798 Singapore Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin300072 China Nanyang Technological University School of Electrical and Electronic Engineering Singapore639798 Singapore National Engineering Research Center for Robot Visual Perception and Control Changsha410082 China Hunan University College of Electrical and Information Engineering Changsha410082 China

This notes focuses on the issue of whether or not the state of a plant can be estimated when the inputs of communication channel involve noise and disturbance simultaneously. It turns out to calculate the maximum capacity of stationary memoryless multiple quasi-access channel under nonstochastic information theory. It is shown that the maximum information rate over such a channel is determined by a convex hull spanned by the zero-error capacities which are specified on the underlying sample spaces. Moreover, the state of a plant could be estimated provided that the maximal information rate is strictly greater than the topology entropy. © 1963-2012 IEEE.

关键词： Communication channels (information theory)

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A closed loop inverse kinematics and control scheme for one class of offset-joint 7-DOF redundant manipulator

A closed loop inverse kinematics and control scheme for one ...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zheng, Yukun Yuan, Hongxiang Song, Rui Ma, Xin Li, Yibin Shandong University Center for Robotics School of Control Science and Engineering Jinan250061 China

ISBN: (纸本)9781728163215

This paper presents a position-based motion control method using the closed-loop inverse kinematics (CLIK) algorithm, which is used for solving one class of offset joint 7 degrees of freedom (DOF) robot. Dual-arm robot is one of the research topics in the field of robotics. In the article, we designed a kind of human dual-arm robot with 14-DOF and studied its kinematics with one of the arms. The inverse kinematic can be solved by using the pseudo-inverse matrix, and the numerical solution can be obtained. In the simulation, we adopt position + velocity scheme and at the same time consider the desired orientation as the posture limitation. This algorithm not only can accurately track the target trajectory, but also can avoid the effect of singularity points. Simulation results for one class end biasing 7-DOF robot demonstrates the effectiveness of the scheme, even for singularities. © 2019 IEEE.

关键词： Inverse kinematics

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Flexible MPC-based conflict resolution using online adaptive ADMM

arXiv

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arXiv 2021年

作者： An, Jerry Giordano, Giulia Liu, Changliu Robotics Institute Carnegie Mellon University Pittsburgh United States Delft Center for Systems and Control Delft University of Technology Delft Netherlands Department of Industrial Engineering University of Trento Italy

Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. However, existing methods generally do not fully utilize optimization in decentralized conflict resolution. We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive control (MPC). OA-ADMM is tailored to online systems, where fast and adaptive real-time optimization is crucial, and allows the use of safety information about the physical system to improve safety in real-time control. We prove convergence in the static case and give requirements for online convergence. Combining OA-ADMM and MPC allows for robust decentralized motion planning and control that seamlessly integrates decentralized conflict resolution. The effectiveness of our proposed method is shown through simulations in CARLA, an open-source vehicle simulator, resulting in a reduction of 47.93% in mean added delay compared with the next best method. © 2021, CC BY-NC-ND.

关键词： Model predictive control

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LMI-Based Design of Output Robust controller with Static Anti-Windup*

LMI-Based Design of Output Robust Controller with Static Ant...

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Nonlinearity, Information and robotics, (NIR) International Conference

作者： Mikhail A. Kakanov Oleg I. Borisov Anton A. Pyrkin Faculty of Control Systems and Robotics St. Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

The article discusses the development of a robust algorithm for controlling the system output in the presence of restrictions on the input signals. The nominal controller contains an integral component to compensate for the external disturbance. Undesired effects of saturation of the input signals are compensated by the static anti-windup correction sheme. An approach is proposed for adjusting the coefficient of anti-windup correction based on the tools of linear matrix inequalities. The effectiveness of the proposed approach is illustrated by the example of an autopilot system by surface vessel.

关键词： Interference Tools Linear matrix inequalities Robots

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Flexible Content-Aware Image Synthesis for Maritime Tasks with Diffusion Models

SSRN

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SSRN 2024年

作者： Xue, Zhenfeng Hu, Yuanqi Lu, Ankang Chen, Zhuo Zang, Ying Miao, Zhonghua School of Mechatronic Engineering and Automation Shanghai University No.99 Shangda Road Shanghai200444 China School of Information Engineering Huzhou University No.759 Second Ring East Road Huzhou313000 China Research Center for Marine Robotics Huzhou Institute of Zhejiang University No.819 Xisaishan Road Huzhou313098 China School of Control Science and Engineering Zhejiang University No.38 Zheda Road Hangzhou310027 China

Maritime environmental perception suffers greatly from data lack due to the high cost of data collection at sea. In this paper, a novel image synthesis method is proposed to automatically generate target images with diverse foreground and background. Specifically, foreground images for various poses are generated using a diffusion model, presenting different modalities of the detected target. The environment and weather conditions of the background image are flexibly adjusted by inputting semantic prompts to another diffusion model. Then a 3D affine diffusion model is proposed for effective fusion of foreground and background. This module calculates the size and position of the foreground image in the background image through affine transformation, and utilizes the excellent image fusion ability of diffusion model to achieve high-quality image synthesis. As a result, a set of dynamically variable foreground and background images are generated to increase the diversity of maritime object detection samples. Extensive experiments are conducted to verify the effectiveness of image synthesis algorithms, and this method can also serve downstream tasks, effectively improving the accuracy of maritime environmental perception algorithms. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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