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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Zhang, Yu Zhang, Xuefang Feng, Shiyu Shen, Qiguang Dang, Shaobo Xu, Kun Wuhan Institute of Post and Telecommunications Wuhan430074 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenZhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Siat Branch China Guangxi University of Science and Technology College of Automation China

ISBN: (纸本)9798350327182

Dynamic interference by moving objects and localization failure induced by low-texture scenes are two urgent problems to be solved in the practical applications of visual simultaneous localization and mapping (SLAM). This study proposes a robust tracking and localization approach with a monocular camera sensor that leverages points within extracted lines and planes to deal with the challenges of dynamic objects and low-texture features. Firstly, we use a line segment detector and an improved CNN-based plane segmentation network to extract lines and planes, respectively, without detecting dynamic objects, thus avoiding complex semantic models. Then, the tracking thread tightly uses plane and line information to promote front-end pose tracking. Finally, the geometric constraint is added to the BA constraints in the pose optimization stage to enhance the robustness. We have evaluated the proposed approach on public datasets and real-scene experiments. Results demonstrate that our method outperforms several state-of-the-art dynamic filtering-based methods in highly dynamic scenes, significantly improving robustness and positioning accuracy. © 2023 IEEE.

关键词： Textures

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Squeeze-in Functionality for a Soft Parallel Robot Gripper

Squeeze-in Functionality for a Soft Parallel Robot Gripper

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Metodi Netzev Alexandre Angleraud Roel Pieters Cognitive Robotics group Tampere University Unit of Automation Technology and Mechanical Engineering Tampere Finland

Grasping parts of inconsistent shapes, sizes and weights securely requires accurate part models and custom gripper fingers. Compliant grippers are a potential solution; however, each design approach requires the solution of unique problems. In this case, the durability and reliability of half lips (at least 1400 cycles) to perform consistently as springs of a specified stiffness (0.5N/mm) and displacement (5mm). Moreover, the challenge of low and small (3mm, 0.01kg bolt or Allen key) objects is addressed through vertical squeeze-in, implemented using an incline, lip and flex limiter as part of a 3D printed TPC spring. The squeeze-in phenomena are verified on large objects through a 30mm, 1.66kg common rail. Experimental results demonstrate the reliability when given a human-specified location for gripping, without the need for jigs or fixtures. Finally, the tested design is assessed for potential fulfillment of 7 of the United Nations sustainable development goals.

关键词： Rails Parallel robots Flexible printed circuits Service robots Lips Grasping Reliability

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Multi-rotor UAV path planning based on Model Predictive Control and Control Barrier Function

Multi-rotor UAV path planning based on Model Predictive Cont...

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Chinese Control and Decision Conference, CCDC

作者： Shengxi Li Yang Chen Yanhua Yang Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

The safety of unmanned aerial vehicles (UAVs) path planning in autonomous urban flight is challenging due to the presence of buildings. Previous studies have focused more on the distance between buildings and UAVs, but ignored the effect of UAV state on path planning. In this paper, we propose a path planning method based on model predictive control (MPC) and control barrier functions (CBF) for UAVs to effectively address the challenge of autonomous UAVs flight. By incorporating CBF and MPC, the MPC optimization is formulated to predict the UAV's flight path based on the current state of the UAV in the receding horizon before considering obstacle avoidance. The CBF constraint guarantees that there are no collisions between the UAV and obstacles, and that the MPC creates an optimal path. This method helps planners design control strategies that meet safety requirements. It improves the spatial configuration between the UAV and nearby obstacles, ensuring that they remain outside the safe range. The method has been verified through simulation and real-world experiments, demonstrating that the UAV maintains a safe distance from obstacles and can also find the shortest path during its flight.

关键词： Buildings Predictive models Autonomous aerial vehicles Path planning Robustness Safety Planning

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

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

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第43届中国控制会议

作者： Liujun Tao Huaiyu Wu Xiujuan Zheng Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

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(Graph SAGE),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.

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

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System Research of Pear Picking Robot 4

System Research of Pear Picking Robot

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4th International Conference on robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Chai, Jianfei Chen, Yi Zhang, Yunyou Wu, Jun Hu, Xiaomei Information Technology Office School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9781450398343

China is a big producer and consumer of pears, and Chinese pear industry has long been based on labor as the main productive force. With the decline of the demographic dividend, labor costs have gradually increased, and labor efficiency has gradually become difficult to meet the needs of modern production. The development of a pear picking robot and its application to actual picking operations is an urgent research. Based on the investigation of the planting environment of pear orchards, this paper designs and builds a pear picking robot. The picking robot consists of a right-angle mechanical arm, a moving system, a control system, and a visual recognition system. On account of the deep learning algorithm, it can accurately identify and locate the pears, and then pick the fruit in a way that the external force is released, so as to realize the damage free picking of the pears. The Outdoor picking experiment shows that the picking robot designed in this paper has a good picking rate in different time periods. © 2022 ACM.

关键词： Object detection

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Security Fence Inspection at Airports Using Object Detection

Security Fence Inspection at Airports Using Object Detection

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IEEE Winter Applications and Computer Vision Workshops (WACVW)

作者： Nils Friederich Andreas Specker Jürgen Beyerer Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Fraunhofer IOSB Fraunhofer Center for Machine Learning Karlsruhe Institute of Technology Institute for Anthropomatics and Robotics

To ensure the security of airports, it is essential to protect the airside from unauthorized access. For this pur-pose, security fences are commonly used, but they require regular inspection to detect damages. However, due to the growing shortage of human specialists and the large man-ual effort, there is the need for automated methods. The aim is to automatically inspect the fence for damage with the help of an autonomous robot. In this work, we explore object detection methods to address the fence inspection task and localize various types of damages. In addition to evaluating four State-of-the-Art (SOTA) object detection models, we analyze the impact of several design criteria, aiming at adapting to the task-specific challenges. This in-cludes contrast adjustment, optimization of hyperparameters, and utilization of modern backbones. The experimental results indicate that our optimized You Only Look Once v5 (YOLOv5) model achieves the highest accuracy of the four methods with an increase of 6.9% points in Average Precision (AP) compared to the baseline. Moreover, we show the real-time capability of the model. The trained models are published on GitHub: hups://***/IN-Friederichlairport_fence_inspection.

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Machine learning-supported CFD optimization of heat transfer in a pipe with a corrugated wall shape 26

Machine learning-supported CFD optimization of heat transfer...

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26th Fluid Mechanics Conference, FMC 2024

作者： Kamiński, P. Li, Y. Wawrzak, K. Tyliszczak, A. Noack, B.R. Department of Thermal Machinery Czȩstochowa University of Technology al. Armii Krajowej 21 Czȩstochowa42-201 Poland Artificial Intelligence and Aerodynamics School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China

The paper focuses on using machine learning to optimize the corrugation of a heated pipe wall to improve the uniformity of temperature distribution while minimizing pressure losses. The corrugation is characterized by a five-parameter function, allowing for extensive variations in wall shape. The machine learning process employs the Bayesian optimization method, iteratively working alongside the ANSYS Fluent code to perform RANS computations. A two-parameter cost function, which includes temperature variation and pressure drop, is used to define the optimization goal. The study demonstrates that substantial variations in flow patterns occur depending on the type of corrugation. An optimal set of corrugation parameters, ensuring the most uniform temperature distribution with minimal pressure losses, is determined within an optimization loop consisting of 1200 RANS simulations. The optimal configuration is characterized by a smooth waviness oriented outside the original wall location in a straight pipe. It ensures more than three times smaller temperature variation at only minor increase in the pressure drop. © Published under licence by IOP Publishing Ltd.

关键词： Drops

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Inverse Origami Design Model for Soft Robotic Development

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Soft robotics 2024年第1期11卷 131-139页

作者： Hu, Qiqiang Li, Junyang Tao, Jian Dong, Erbao Sun, Dong Department of Biomedical Engineering City University of Hong Kong Hong Kong Department of Precision Machinery and Precision Instrumentation University of Science and Technology of China Hefei China Department of Electronic Engineering Ocean University of China Qingdao China CAS Key Laboratory of Mechanical Behavior and Design of Materials School of Engineering Science University of Science and Technology of China Hefei China Centre for Robotics and Automation Shenzhen Research Institute of City University of Hong Kong Shenzhen China

Origami provides an opportunity to construct a wide range of 3D functional structures by folding a flat sheet. It can be used to develop various soft functional robots by combining soft smart actuators. However, a simple and an effective model that can address the challenging problem of designing origami patterns to connect origami design with robotics is lacking, thereby greatly increasing the threshold of soft origami robots and hindering its development. This study proposes an easy-to-use inverse origami design model to generate the flat crease pattern from the desired folded shape automatically while simulating origami morphing by simply providing the shape parameters or 2D shape graphics. This method overcomes the difficulty of origami design and enables a close connection between origami and robotics. Through this method, various soft origami robots can be developed with low design complexity and time cost to achieve different functions, thereby promoting the development of soft origami robots. © 2024 Mary Ann Liebert Inc.. All rights reserved.

关键词： Robots

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Discovering Syntactic Interaction Clues for Human-Object Interaction Detection

Discovering Syntactic Interaction Clues for Human-Object Int...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jinguo Luo Weihong Ren Weibo Jiang Xi'ai Chen Qiang Wang Zhi Han Honghai Liu Harbin Institute of Technology Shenzhen State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, Vision-Language Model (VLM) has greatly ad-vanced the Human-Object Interaction (HOI) detection. The existing VLM-based HOI detectors typically adopt a hand-crafted template (e.g., a photo of a person [action] a/an [object]) to acquire text knowledge through the VLM text encoder. However, such approaches, only encoding the action-specific text prompts in vocabulary level, may suffer from learning ambiguity without exploring the fine-grained clues from the perspective of interaction context. In this paper, we propose a novel method to discover Syntactic Interaction Clues for HOI detection (SICHOI) by using VLM. Specifically, we first investigate what are the essen-tial elements for an interaction context, and then establish a syntactic interaction bank from three levels: spatial relationship, action-oriented posture and situational condition. Further, to align visual features with the syntactic interaction bank, we adopt a multi-view extractor to jointly aggre-gate visual features from instance, interaction, and image levels accordingly. In addition, we also introduce a dual cross-attention decoder to perform context propagation be-tween text knowledge and visual features, thereby enhancing the HOI detection. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on HICO-DET and V-COCO.

关键词： Visualization Vocabulary Computer vision Aggregates Detectors Syntactics Feature extraction

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Dynamic Neural Curiosity Enhances Learning Flexibility for Autonomous Goal Discovery

arXiv

引用

arXiv 2024年

作者： Houbre, Quentin Pieters, Roel Tampere University Tampere Finland Department of Automation Technology and Mechanical Engineering Cognitive Robotics Tampere University Tampere33720 Finland

The autonomous learning of new goals in robotics remains a complex issue to address. Here, we propose a model where curiosity influence learning flexibility. To do so, this paper proposes to root curiosity and attention together by taking inspiration from the Locus Coeruleus-Norepinephrine system along with various cognitive processes such as cognitive persistence and visual habituation. We apply our approach by experimenting with a simulated robotic arm on a set of objects with varying difficulty. The robot first discovers new goals via bottom-up attention through motor babbling with an inhibition of return mechanism, then engage to the learning of goals due to neural activity arising within the curiosity mechanism. The architecture is modelled with dynamic neural fields and the learning of goals such as pushing the objects in diverse directions is supported by the use of forward and inverse models implemented by multilayer perceptrons. The adoption of dynamic neural fields to model curiosity, habituation and persistence allows the robot to demonstrate various learning trajectories depending on the object. In addition, the approach exhibits interesting properties regarding the learning of similar goals as well as the continuous switch between exploration and exploitation. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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