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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Kexin Chen, Teng Chen, Xin Zhang, Guoteng Li, Yibin Rong, Xuewen Center for Robotics School of Control Science and Engineering Shandong University China

Due to the high flexibility of quadruped robots compared with some traditional robots, it has become an important branch in the field of mobile robot research. Target detection and tracking technology is important for the environment perception and autonomous motion tasks of quadruped robots. To achieve effective tracking of a specific target, the premise is that the target is accurately detected. In this article, a target detection algorithm based on deep learning for quadruped robot person tracking is proposed. After the targets are detected, a simple but accurate selection strategy is used to extract the certain target to be tracked from other targets. The visual distance and angle information from the target is used to realize the autonomous tracking. In the part of experiment, we conducted experiments based on the changeable outdoor environment, which included interference factors such as light changes, people crossing, obstruction by obstacles. During the experiments, the SDUQuad-48 quadruped robot can simultaneously realize the stable tracking of a specific moving target based on our algorithm with a speed of 0.8 m/s. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Target tracking

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Multi-Aspect Optimal Sliding Mode controller for a Mecanum Wheeled Robot 11

Multi-Aspect Optimal Sliding Mode Controller for a Mecanum W...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Allahyari, Sohrab Rahmanei, Hamid Moosavian, S. Ali A. K. N. Toosi University of Technology Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems Laboratory Dept. of Mechanical Engineering Tehran Iran

ISBN: (纸本)9798350308105

Four-wheel drive Mecanum robots have gained attention due to their ability to move in all directions, which allows them to work in tight and complex environments. For this reason, ensuring precise control of these robots along predefined trajectories is crucial. This work presents a comparative study on the effects of parametric uncertainties, external disturbances, measurement noises, and initial deviations on the trajectory-Tracking performance of a 4-wheel drive Mecanum robot. First, the kinematic and kinetic models of the Mecanum robot are introduced. Then, the integral sliding mode control is designed, and proof of stability is provided. In addition, we explore other control strategies, including PID, feedback linearization, sliding mode, and adaptive sliding mode control, that are designed for this robot. Furthermore, the particle swarm optimization algorithm is hired for the controllers with a cost function as the total sum of average error, maximum error, and final error. Optimization has been done on all controllers to compare the results fairly. To appraise the effectiveness of the proposed control optimization methods, simulations are carried out in MATLAB/Simulink. The results reveal that the proposed ISMC method performs best in both defined scenarios compared to other controllers. However, it must be noted that the ISMC controller exhibits limitations when facing significant uncertainties in the dynamic model. This study underscores the effectiveness of our proposed control method and provides valuable insights into the practical implementation controller for the Mecanum robot in real-world applications. © 2023 IEEE.

关键词： Three term control systems

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Language Guided Grasping of Unknown Concepts Based on Knowledge System 16th

Language Guided Grasping of Unknown Concepts Based on Know...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Huang, Saike Zhu, Zhenwei Liu, Jin Wang, Chaoqun Zhou, Fengyu Center for Robotics School of Control Science and Engineering Shandong University No. 17923 Jingshi Road Jinan250061 China

ISBN: (纸本)9789819964949

Language guided grasping in cultter environment has become a hot topic for robotic arms, where the robot is supposed to grasp a specific object according to the language instruction. However, existing studies typically fail on grasping unknown objects that are not recognized by the detection model, which post higher requirements of the learning unknown objects from external knowledge. In this paper, unlike traditional target detection algorithms, our system identifies unknown objects through a knowledge-driven approach. We present a knowledge-learning driven system that guides the robot in learning new concepts to grasp unknown objects from pre-built multimodal knowledge base. Specifically, we first retrieve the image knowledge from Internet and text description knowledge from human experts to construct the multimodal knowledge base. Based on the question context, the model will query the object concept knowledge from the knowledge base. Once the concept is known, the model will ground the object by a multimodal fusion module. In contrast, to address the issue of novel concept, we propose a knowledge fusion module that combines BERT, GCN and ResNet to acquire semantic text knowledge, structured text knowledge, and image knowledge derived from the knowledge base. Subsequently, we utilized the re-calibration multimodal fusion module to ground unknown objects. Finally, conditioned on the grounding context, we employ a well-trained grasp network to derive the accurate grasping pose to perform the grasping task. Experiments conducted on the simulation and real-world scenarios demonstrate that our knowledge-learning driven system is effective in assisting robots to learn unknown concepts and achieve successful grasping tasks. A demo video is available online (https://***/Fsc4nZyY9LI ). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robots

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Waste Sorting Automation with Visual Sensor and RFID 3

Waste Sorting Automation with Visual Sensor and RFID

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3rd International Conference on Advanced Electrical Engineering, ICAEE 2024

作者： Seray, Seif Eddine Bey, Ishak Akli, Isma University of M'hammed Bouguerra Department of Control and Power Boumerdes Algeria Center for Development of Advanced Technologies Robotics and Industrial Automation Division Algiers Algeria

ISBN: (纸本)9798350349351

The quantity of domestic wastes is constantly increasing, resulting on overload of wastes. Recycling became imperative for ecologic and economical considerations in order to apply circular economy rules. In general, humans are recruited in waste sorting plants for performing the dangerous and tedious sorting tasks. The automation is, thus, necessary. This paper deals with the problem of waste sorting automation. The proposed solution consists of waste recognition and classification using YOLOv8, and human presence detection using RFID (Radio frequency Identification) tags. The proposed solution is validated in simulation. The virtual robot performs a task in order to simulate the behavior of a real robot with high fidelity, depending on the information coming from the RFID tags and a visual sensor. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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C2FNet: Coarse-to-Fine Keypoint Localization Network for Monocular 6D Object Pose Estimation

C2FNet: Coarse-to-Fine Keypoint Localization Network for Mon...

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2023 China Automation Congress, CAC 2023

作者： Sun, Jiahao Ma, Xin Li, Yibin Center for Robotics School of Control Science and Engineering Shandong University China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan China

ISBN: (纸本)9798350303759

Estimating the 6D object pose from a singular RGB image is a fundamental task in the field of computer vision. Recent studies have demonstrated that keypoint-based methods exhibit remarkable efficacy. Such methodologies initially identify keypoints and subsequently deduce the object's pose by addressing a Perspective-n-Point (PnP) problem. Nonetheless., the prevalent approach of directly regressing the 2D coordinates of keypoints renders these methods sensitive to occlusions. Instead., we inaugurate a coarse-to-fine keypoint localization network (C2FNet)., where we utilize the predicted keypoint offsets from the first stage as input to deformable convolutional networks (DCN) in the second stage., to further capture geometric features and enhance spatial relationships between keypoints., thus outputting accurate and robust keypoint coordinates. Experiments indicate that the proposed approach surpasses existing advanced methods in terms of performance on the LINEMOD and Occlusion LINEMOD datasets. © 2023 IEEE.

关键词： Computer vision

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Learning-based robot assembly method for peg insertion tasks on inclined hole using time-series force information

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Biomimetic Intelligence & robotics 2025年第1期5卷 116-123页

作者： Zhifei Shen Zhiyong Jiang Jingwang Zhang Jun Wu Qiuguo Zhu Polytechnic Institute Zhejiang UniversityHangzhou 310015China Robotics Engineering Center the 21st Research Institute of China Electronics Technology Group CorporationShanghai 200233China Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

This paper presents a novel method for learning force-aware robot assembly skills,specifically targeting the peg insertion task on inclined *** the peg insertion task involving inclined holes,we employ one-dimensional convolutional networks(1DCNN)and gated recurrent units(GRU)to extract features from the time-series force information during the assembly process,thereby identifying different contact states between the peg and the *** to the identification of contact states,corresponding pose adjustments are executed,and overall smooth interaction is ensured through admittance *** assembly process is dynamically adjusted using a state machine to fine-tune admittance control parameters and seamlessly switch the assembly *** the utilization of dual-arm clamping,we conduct key unlocking experiments on bases inclined at varying *** results demonstrate that the proposed method significantly improves the accuracy and success rate of state recognition compared to previous methods.

关键词： Manipulator assembly Peg insertion Force information Contact state Dual-arm

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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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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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Direct fabrication of flexible tensile sensors enabled by polariton energy transfer based on graphene nanosheet films

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Nanotechnology and Precision Engineering 2023年第1期6卷 1-11页

作者： Xi Zhang Junchi Ma Wenhao Huang Jichen Zhang Chaoyang Lyu Yu Zhang Bo Wen Xin Wang Jing Ye Dongfeng Diao College of Mechatronics and Control Engineering Shenzhen UniversityShenzhen 518060China Research Center of Medical Plasma Technology Shenzhen UniversityShenzhen 518060China Shenzhen Milebot Robotics Co. Ltd.Shenzhen 518060China

A fundamental problem in the direct manufacturing of flexible devices is the low melting temperature of flexible substrates,which hinders the development of flexible *** here is an electron-cyclotron-resonance sputtering system that can batch-fabricate devices directly on flexible substrates under a low temperature by virtue of the polariton energy transfer between the plasma and the *** graphene nanosheet-embedded carbon(F-GNEC)films are manufactured directly on polyimide,polyethylene terephthalate,and polydimethylsiloxane,and how the substrate bias(electron energy),microwave power(plasma flux and energy),and magnetic field(electron flux)affect the nanostructure of the F-GNEC films is investigated,indicating that electron energy and flux contribute to the formation of standing graphene nanosheets in the *** films have good uniformity of distribution in a large size(17 mm×17 mm),and tensile and angle sensors with a high gauge factor(0.92)and fast response(50 ms)for a machine hand are obtained by virtue of the unique nanostructure of the F-GNEC *** work sheds light on the quantum manufacturing of carbon sensors and its applications for intelligent machine hands and virtual-reality technology.

关键词： Direct flexible fabrication Graphene nanosheets film Polariton energy transfer Flexible sensor Quantum manufacturing

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Diabetic Foot Ulcer Unsupervised Segmentation with Vision Transformers Attention 4th

Diabetic Foot Ulcer Unsupervised Segmentation with Vision T...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Brodzicki, Andrzej Kucharski, Dariusz Moniak, Bartlomiej Kostuch, Aleksander Noworolnik, Filip Anna, W. Jaworek-Korjakowska, Joanna Department of Automatic Control and Robotics AGH University of Kraków Kraków Poland Center of Excellence in Artificial Intelligence AGH University of Kraków Kraków Poland

ISBN: (纸本)9783031808708

The Diabetic Foot Ulcer Challenge is a global computer vision competition organised to raise awareness of the complications caused by diabetes and motivate researchers to develop solutions that might help in the treatment. In 2024, the challenge focused on self-supervised learning methods and synthetic data, as the process of collecting and labelling medical data is a tedious one. In this research, we present a novel, two-stage method for self-supervised DFU segmentation. It is based on a Vision Transformer attention from classification pre-task, followed by U-Net weakly supervised segmentation refinement. Our solution took 3rd place on the challenge leaderboard, with a 0.4796 Dice Score on the test set. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Distribution transformers

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