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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhou, Jie Fang, Yuan Chen, Yang Li, Yao Li, Bing Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong School of Mechanical Engineering and Automation Harbin Institute of Technology Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

ISBN: (纸本)9781665481090

Bio-robots continue to receive attention because of their small size and low power consumption. Unfortunately, it currently faces two major obstacles before practical application. The first is the habituation issue, insects are difficult to be effectively controlled for a long time by electrical stimuli signals. The other one is the lack of model for motion control. In this research, we established a stimuli-response model for cockroach bio-robot based on long time current. A more convenient optic lobe implantation method has been proposed, which has lower attenuation and better stimuli effect. Constant electrical stimuli also significantly increased the number of effective controls for crawling. A cockroach bio-robot stimuli distance response model was established and validated with experiment data. Five parameters were including in this model, namely sex, length, weight, current amplitude and equivalent stiumated-number. This model can predict the movement distance of biological robots well. © 2024 IEEE.

关键词： Microrobots

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Autonomous Door Opening with a Rescue Robot

Autonomous Door Opening with a Rescue Robot

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International Conference on Autonomous Robots and Agents, ICARA

作者： Renat Abdulganeev Roman Lavrenov Alexandra Dobrokvashina Yang Bai Evgeni Magid Intelligent Robotics Department Laboratory of Intelligent Robotic Systems (LIRS) Institute of Information Technology and Intelligent Systems Kazan Federal University Kazan Russia Graduated School of Information Science and Technology Osaka University Osaka Japan

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

Autonomy of a mobile robot can only be achieved if it independently deals with obstacles. This article discusses an experience of autonomously opening doors by the Russian crawler rescue robot Servosila Engineer. A door and handle detection is based on the YOLO v3 algorithm. The robot motion and stopping at a predefined distance are guided by a laser range finder. After gripping the door handle, the door could be opened using either a pulling or pushing method. Recommended optimal distance to the door, an angle at which the robot turns towards the door and a stopping point for the robot were experimentally set and evaluated in an indoor office environment.

关键词： Robot motion YOLO Visualization Lasers Crawlers Robot sensing systems Optical sensors

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Resilient Event-Triggered Model Predictive Control for Adaptive Cruise Control Under Sensor Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 807-809页

作者： Zhijian Hu Rong Su Kai Zhang Zeyuan Xu Renjie Ma School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technologyand the Center of Ultra-Precision Optoelectronic Instrument EngineeringHarbin Institute of TechnologyHarbin 150001China Department of Control Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Chemical and Biomolecular Engineering National University of SingaporeSingapore 117585Singapore State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China IEEE

Dear Editor,This letter addresses the resilient model predictive control(MPC)problems for adaptive cruise control(ACC)systems under sensor *** the light of vulnerabilities of ACC systems to sensor attacks,an intrusion detection mechanism is proposed at the controller side to distinguish abnormal ***,the robust control gains are derived to design the terminal region constraint for MPC.

关键词： Trigger Event constraint

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Scoliosis detection method based on fringe projection profilometry and deep learning

Scoliosis detection method based on fringe projection profil...

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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Wang, Meng He, Dong Xu, Shaolei Tang, Qijian Peng, Xiang Liu, Xiaoli State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Guangdong China Key Laboratory of Intelligent Optical Measurement and Detection of Shenzhen Shenzhen Institute of Information Technology Shenzhen Guangdong China School of Sino-German Robotics Shenzhen Institute of Information Technology Shenzhen Guangdong China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

In this paper, a fringe projection profilometry (FPP) system for three-dimensional (3D) imaging of the human back is proposed to assess scoliosis, which has developed into one of the top four health concerns affecting adolescents. The FPP system includes a binocular stereo vision system and an infrared projector, with a Field Programmable Gate Array (FPGA) based synchronization control system enabling a 240Hz acquisition frame rate. This high frame rate mitigates the impact of involuntary body movement on 3D image quality. Various features can be extracted from the generated 3D model of the back for quantitative postural assessment. Specifically, a deep learning approach is employed to analyze the extracted trunk rotation angle curve for scoliosis detection. Experimental results demonstrate a correlation between this method and X-ray detection, offering a promising exploratory approach to reduce reliance on X-ray imaging in scoliosis monitoring. © 2025 SPIE.

关键词： Stereo image processing

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Integrated Batching, Pick Sequencing and Robot Assignment for Robotic Warehouse Automation

Integrated Batching, Pick Sequencing and Robot Assignment fo...

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Conference on information and Communication technology (CICT)

作者： Rishi Gupta Surya Kant Shubhang Singh Rahul Kala Gora Chand Nandi Center for Intelligent Robotics Indian Institute of Information Technology Allahabad Allahabad India Center for Autonomous Systems ABV-Indian Institute of Information Technology and Management Gwalior Gwalior India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

Future warehouses will see robots autonomously picking up all the items ordered by the user and delivering them for packaging, sorting and delivery. This requires warehouses to schedule the robots to autonomously cater to all customer orders. In this paper, we solve the problem of batching orders, where several user orders are clubbed into one to be collectively picked by one robot, scheduling the visit of the robot to pick up the desired items and assigning one robot to carry the operation. Each robot is assumed to have a small payload capacity that restricts the number of orders that can be batched. Each item is assumed to be found at several locations, and the robot can select anyone of the locations. The proposed solution uses heuristics to create initial batches that are further optimised by using Genetic Algorithm. Genetic Algorithm also jointly optimises the batching and item pick sequencing. The selection of one out of several sites is done by using a Dynamic Programming approach. The solution is post processed by using a Dynamic Programming solution for optimally scheduling the robot visits within every batch. The experimental results show that the proposed solution scales to a large number of orders because of the use of heuristics, while the results are better than the use of greedy heuristic approaches.

关键词： Sequential analysis Schedules Heuristic algorithms Packaging Dynamic programming information and communication technology Robots Genetic algorithms Sorting Payloads

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Calculation Method of Road Distress Area Based on Monocular Vision 3

Calculation Method of Road Distress Area Based on Monocular ...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Wang, Lei Hu, Yuxin Zhang, Zhaowei Jiang, Lin School of Mechanical and Automation Engineering Wuhan University of Science and Technology Wuhan430081 China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan430081 China

ISBN: (纸本)9781510667563

Proposed a method for calculating the area of road diseases using a monocular camera. The camera and the road plane are calibrated to obtain the mapping relationship between pixel points and spatial points;the Mask R-CNN is used to detect and segment the road disease;the inverse perspective transformation is used to convert the target image into a bird’s-eye view to obtain the relationship between the pixel scale and the real scale;binarize the bird’s-eye view according to the segmentation effect, and the area of the road disease could be obtained through calculating the number of target pixels. It has been verified that the calculation error of the algorithm is about 2.1675% in the near vicinity. © 2023 SPIE.

关键词： Birds

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Remaining Useful Life Prediction of Lithium-ion Batteries Based on Multi-graph-network model 43

Remaining Useful Life Prediction of Lithium-ion Batteries Ba...

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43rd Chinese Control Conference, CCC 2024

作者： Tao, Liujun Wu, Huaiyu Zheng, Xiujuan Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan430081 China

ISBN: (纸本)9789887581581

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for their safety and reliability, and it's an important issue of concern for battery management systems. The existing RUL prediction is mainly concentrated on a single source characteristics or a single model, and generally only the time features of the battery Health Indicators (HIs) are considered. This article proposes a multi-feature and multi-graph neural network fusion method for predicting the RUL of batteries. First of all, extract HIs from multiple aspects such as voltage, current, temperature curve and other aspects during partial charging and discharging processes, and construct HIs graph. Secondly, preliminary predictions of RUL were made using Graph Convolutional Network (GCN), Graph Attention Network (GAT), and Graph Sample and Aggregate Network (GraphSAGE), respectively. In order to improve the prediction accuracy of the model, the model extracts spatial features from non-homologous sources and constructs an HI graph as the primary input. Inspired by the advantages of multi-model fusion, random forest regression is used for multi model fusion. To verify the effectiveness of the model, comparative experiments were conducted on the Massachusetts institute of technology (MIT) battery dataset. The results indicate that the proposed method has better accuracy and stronger robustness in RUL prediction. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Lithium-ion batteries Multi graph neural network Multi source spatial features Prediction of remaining useful life Random forest regression

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The System of Coordinated Operation of Two Underwater Vehicles in a Complex Environment

The System of Coordinated Operation of Two Underwater Vehicl...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Filaretov, Vladimir Katsurin, Alexey Institute of Automation and Control Processes Feb Ras Vladivostok State University Laboratory of Robotic Systems Laboratory of Automation and Robotics Vladivostok Russia Far Eastern Federal University M.D. Ageev Institute for Marine Technology Problems Feb Ras Laboratory of Intelligent Information Systems for Marine Robots Department of Automation and Robotics Vladivostok Russia

ISBN: (纸本)9798350382457

The paper discusses methods of coordinated operation of two autonomous underwater vehicles, one of which is equipped with a multi-link manipulator and a vision system, and the second - auxiliary - only a vision system. These methods ensure the joint operation of two autonomous underwater vehicles both in the mode of semi-automatic positional remote control, and in the mode of automatic execution of operations with various objects. At the same time, work is considered in situations when the vision system of the main underwater vehicle does not allow observing the object of work, which is nevertheless located in the working area of the manipulator. In this case, the auxiliary inspection underwater vehicle with its own vision system is used to monitor the object. In the semi-automatic remote control mode, the manipulator automatically takes into account the current spatial orientation of the optical axis of the vision system of the inspection underwater vehicle, as well as the spatial orientations of both underwater vehicles at yaw angles in the absolute coordinate system. And in automatic mode, an accurate transfer of the spatial location and orientation of the object, which are determined using the vision system of the inspection underwater vehicle, is additionally provided to the associated coordinate system of the main underwater vehicle. It is possible to identify and eliminate errors that occur during the operation of the vision system and navigation systems of both underwater vehicles. The results of experimental studies have confirmed the operability of the proposed methods. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Local Path Planning of Unmanned Surface Vehicle Based on Improved DWA Algorithm

Local Path Planning of Unmanned Surface Vehicle Based on Imp...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Ji, Chenjia Zhang, Lanyong College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819622351

An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters that cannot effectively avoid when encountering unknown dynamic obstacles. Firstly, an autonomous collision avoidance decision-making model was constructed to judge whether the unmanned boat had a collision hazard, and the encounter situation was reclassified. To ensure that the USV can safely pass through the moving obstacle vessel, a collision risk assessment function considering the velocity of dynamic obstacles is introduced. Moreover, a collision avoidance rule function is incorporated into the evaluation function of the collision avoidance algorithm to ensure that the USV adheres to collision avoidance regulations. Ultimately, the simulation results confirm that the enhanced dynamic window method can safely avoid collisions while adhering to collision avoidance rules. This paper proposes an effective solution for collision avoidance behavior of intelligent vessels in near shore waters when facing unknown dynamic obstacles, providing insightful recommendations for subsequent research in maritime sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned surface vehicles

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SESGMS: A Welding Seam Extraction Method Based on Spherical Gaussian Mixture Sampling

SESGMS: A Welding Seam Extraction Method Based on Spherical ...

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Chinese Automation Congress (CAC)

作者： Chuxiong Wu Haotian Zhou Huasong Min Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Accurate seam extraction is crucial for optimizing welding robot performance. Current 3D weld detectors often struggle with geometric approximation challenges and fail to adapt effectively across different weld types, leading to missed or redundant detections that require meticulous threshold adjustments. This paper proposes a weld seam detection method for accurate and robust extraction of weld seam points. Firstly, a neighborhood-weighted spherical projection algorithm is proposed to enhance local feature distinctions within point clouds, improving recognition accuracy of weld seam points. Secondly, a novel Local Reference Frame (LRF) algorithm based on spherical projection is designed to ensure rotational isotropy and facilitate feature comparability across diverse point cloud orientations. In LFR, rotational Spherical Gaussian (SG) closed-form is utilized for hybrid sampling, aiming to achieve efficient, stable, and isotropic 2D transformations of point cloud features. Finally, angle-independent feature vectors are generated by kernel density estimation with a nearest neighbor algorithm to assess symmetry, improving the extraction accuracy of weld. Experimental results on three types of workpieces and seven angular weld models demonstrate that our method excels in adaptive weld seam extraction, particularly in accurately identifying weld seam points.

关键词： Point cloud compression Adaptation models Accuracy Three-dimensional displays Welding Estimation Feature extraction Approximation algorithms Vectors Kernel

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