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A Collision-Aware Cable Grasping Method in Cluttered Environment

A Collision-Aware Cable Grasping Method in Cluttered Environ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Lei Zhang Kaixin Bai Qiang Li Zhaopeng Chen Jianwei Zhang Department of Informatics TAMS (Technical Aspects of Multimodal Systems) Universität Hamburg Agile Robots AG Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We introduce a Cable Grasping-Convolutional Neural Network (CG-CNN) designed to facilitate robust cable grasping in cluttered environments. Utilizing physics simulations, we generate an extensive dataset that mimics the intricacies of cable grasping, factoring in potential collisions between cables and robotic grippers. We employ the Approximate Convex Decomposition technique to dissect the non-convex cable model, with grasp quality autonomously labeled based on simulated grasping attempts. The CG-CNN is refined using this simulated dataset and enhanced through domain randomization techniques. Subsequently, the trained model predicts grasp quality, guiding the optimal grasp pose to the robot’s controller for execution. Grasping efficacy is assessed across both synthetic and real-world settings. Given our model’s implicit collision sensitivity, we achieved commendable success rates of 92.3% for known cables and 88.4% for unknown cables, surpassing contemporary state-of-the-art approaches. Supplementary materials can be found at https://***/cg-cnn/.

关键词： Sensitivity Neural networks MIMICs Grasping Predictive models Robot sensing systems Collision avoidance

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Intelligent Control Systems for Industrial automation and robotics

Intelligent Control Systems for Industrial Automation and Ro...

引用

international conference on Science Technology Engineering and Management (ICONSTEM)

作者： Swarupa Pelleti Jambi Ratna Raja Kumar V. S. Krushnasamy G. Balamuruga Mohan Raj KLH Global Business School Koneru Lakshmaiah Education Foundation (Deemed to be University Estd. U/S. 3 of UGC Act 1956) Category - I NIRF University Ranking: 28 Hyderabad Department of Computer Engineering GenbaSopanraoMoze College of Engineering Pune Department of Electronics and Instrumentation Engineering Dayananda Sagar College of Engineering Bangalore SriManakulaVinayagar Engineering College Puducherry India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This study explores the field of Intelligent Control Systems (ICS) and how they have revolutionized robotics and industrial automation. The benefits of ICS over conventional PID control techniques are examined in this work using mathematical models, actual experiments, and performance measures. After conducting a thorough investigation, we discovered that ICS performs very well in tracking accuracy, requires less control effort, and responds more quickly, highlighting its versatility and efficiency in carrying out high-precision tasks. These discoveries have significant ramifications for the industrial sector, bringing in a new age of operational excellence and bringing ICS into line with the ideas of Industry 4.0. Future research aims to solve ethical and interoperability issues, improve human-robot interaction, integrate machine learning for adaptation, optimize ICS algorithms for particular applications, and guarantee scalability and cost-effectiveness. To sum up, this study not only confirms the promise of ICS but also lays out a roadmap for their further development, making them ready to completely transform the industrial sector and push the boundaries of innovation.

关键词： Technological innovation PI control Service robots Scalability Transforms Mathematical models PD control

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Adaptive Data Optimization for Cross-domain Action Recognition in Low-Light Environment

Adaptive Data Optimization for Cross-domain Action Recogniti...

引用

IEEE conference on Cybernetics and Intelligent Systems

作者： Haoran Liu Huan Yang Danwei Wang School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

Human action recognition (HAR) serves as a foundational task in human-robot interaction applications. However, the challenge intensifies in low-light conditions, where limited dark scene training data are available, making current approaches less effective. In this paper, we propose a cross-domain adaptive data optimization approach to enhance human action recognition in dark lighting conditions. This approach encompasses two core components. The first one is adaptive data enhancement including stretching and zooming optical property transformation at the frame level. The second one is hybrid data filtering which progressively trains the model on data that exhibit a gradation of differences, ranging from minimal to substantial. We perform experiments of the comparison of our method and other baseline methods on the something-something v2 dataset and a small amount of dark scene action recognition data. The experiments demonstrate the effectiveness of our modules and and convenience of the overall pipeline.

关键词： Training Adaptation models Accuracy Training data Lighting Human-robot interaction Seaports

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Human-Aligned Longitudinal Control for Occluded Pedestrian Crossing With Visual Attention

Human-Aligned Longitudinal Control for Occluded Pedestrian C...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Vinal Asodia Zhenhua Feng Saber Fallah Department of Mechanical Engineering Sciences University of Surrey Guildford UK School of Computer Science and Electronic Engineering University of Surrey Guildford UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Reinforcement Learning (RL) has been widely used to create generalizable autonomous vehicles. However, they rely on fixed reward functions that struggle to balance values like safety and efficiency. How can autonomous vehicles balance different driving objectives and human values in a constantly changing environment? To bridge this gap, we propose an adaptive reward function that utilizes visual attention maps to detect pedestrians in the driving scene and dynamically switch between prioritizing safety or efficiency depending on the current observation. The visual attention map is used to provide spatial attention to the RL agent to boost the training efficiency of the pipeline. We evaluate the pipeline against variants of an occluded pedestrian crossing scenario in the CARLA Urban Driving simulator. Specifically, the proposed pipeline is compared against a modular setup that combines the well-established object detection model, YOLO, with a Proximal Policy Optimization (PPO) agent. The results indicate that the proposed approach can compete with the modular setup while yielding greater training efficiency. The trajectories collected with the approach confirm the effectiveness of the proposed adaptive reward function.

关键词： YOLO Training Visualization Adaptation models Pedestrians Pipelines Switches

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Enhancing Human Motion Imitation in Humanoid Robots: A Comparative Study of ANN, ANFIS, and GA-Optimized ANFIS for Inverse Kinematics of the Surena-V Humanoid's Arm

Enhancing Human Motion Imitation in Humanoid Robots: A Compa...

引用

RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Amin Mozayyan Aghil Yousefi-Koma Alireza Naeini Sara Rahmati Kookandeh Center of Advanced Systems and Technologies (CAST) School of Mechanical Engineering University of Tehran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Humanoid robots are increasingly vital in automation, healthcare, and hazardous environments, where teleoperation enables them to replicate human movements for complex tasks. A key challenge in teleoperation is solving inverse kine-matics, which determines the joint angles required for a robotic arm to reach a specific end-effector position. This research addresses the challenge of solving the inverse kinematics of a redundant robotic arm of the Surena-V humanoid robot, to enhance the robot's capability to accurately imitate human arm movements. Using MediaPipe for human pose estimation, wrist coordinates were input into models to calculate joint angles. Three methods were deployed: Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), and ANFIS optimized using a Genetic Algorithm (GA). The GA-optimized ANFIS model achieved the lowest Root Mean Square Error (RMSE) in joint angle prediction, with values of 6.93, 7.23, 12.33, and 0.9, and superior end-effector accuracy with RMSEs of 0.0199 m, 0.0150 m, and 0.0203 m for the x, y, and z coordinates. These results underscore the effectiveness of GA-optimized ANFIS in handling complex nonlinearities, offering significant improvements in humanoid robot performance for teleoperation and human-robot interaction tasks.

关键词： Wrist Adaptation models Accuracy Robot kinematics Humanoid robots Kinematics Artificial neural networks Robot sensing systems End effectors Genetic algorithms

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Metrically Scaled Monocular Depth Estimation through Sparse Priors for Underwater Robots

Metrically Scaled Monocular Depth Estimation through Sparse ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Luca Ebner Gideon Billings Stefan Williams Robotic Systems Lab ETH Zurich Australian Centre for Field Robotics University of Sydney

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this work, we address the problem of real-time dense depth estimation from monocular images for mobile underwater vehicles. We formulate a deep learning model that fuses sparse depth measurements from triangulated features to improve the depth predictions and solve the problem of scale ambiguity. To allow prior inputs of arbitrary sparsity, we apply a dense parameterization method. Our model extends recent state-of-the-art approaches to monocular image based depth estimation, using an efficient encoder-decoder backbone and modern lightweight transformer optimization stage to encode global context. The network is trained in a supervised fashion on the forward-looking underwater dataset, FLSea. Evaluation results on this dataset demonstrate significant improvement in depth prediction accuracy by the fusion of the sparse feature priors. In addition, without any retraining, our method achieves similar depth prediction accuracy on a downward looking dataset we collected with a diver operated camera rig, conducting a survey of a coral reef. The method achieves real-time performance, running at 24 FPS on a NVIDIA Jetson Xavier NX, 160 FPS on a NVIDIA RTX 2080 GPU and 7 FPS on a single Intel i9-9900K CPU core, making it suitable for direct deployment on embedded GPU systems. The implementation of this work is made publicly available at https://***/ebnerluca/uw_depth.

关键词： Surveys Deep learning Accuracy Fuses Graphics processing units Estimation Predictive models

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Finite element modeling and vibration reduction analysis of thin-walled structure with constraining layer damping

Finite element modeling and vibration reduction analysis of ...

引用

2024 8th international conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Ziyang Liu Yulin Liu Haitao Luo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences (CAS) Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Thin-walled structures are widely used in the engineering field, but due to their low stiffness and low damping, they are prone to vibration. The application of constrained layer damping (CLD) surface treatment can effectively improve the vibration suppression ability of thin-walled structures. This paper uses the carbon fiber composite plate as the research object of thin-walled structures and uses finite element method to model the cantilever plate with CLD. The composite element with four nodes and 28 degrees of freedom is used in the modeling. And the comparison of the frequency response and vibration mode obtained by the simulation method in this paper and the Nastran simulation software verifies the correctness of the modeling method. Finally, the optimized shape of the CLD is obtained based on the modal strain of the cantilever plate, verifying the rationality of the CLD configuration method.

关键词： Vibrations Damping Shape Modal analysis Mathematical models Software Finite element analysis Carbon Surface treatment Strain

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Sugar Cane Yield Prediction Using Drone Data Processed by LSTM Algorithm

Sugar Cane Yield Prediction Using Drone Data Processed by LS...

引用

Mechatronics, Control and robotics (ICMCR), international conference on

作者： Kotetsu Arakawa Ryusei Shimizu Shota Kikuchi Genci Capi Graduate School of Science and Engineering Dept. of Mechanical Eng. Hosei University Tokyo Japan

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Sugarcane is an essential crop in southwestern Japan, particularly in Okinawa and Kagoshima, where it sustains local economies and supports large-scale sugar production. Traditional yield prediction relies on labor-intensive field surveys and is increasingly challenged by a shortage of skilled labor, resulting in potential inaccuracies. This work proposes an innovative approach to sugarcane yield prediction by combining Long Short-Term Memory (LSTM) models with Genetic Algorithms (GA) to optimize model performance. Drone-based data collection methods are also explored, leveraging aerial imagery to provide additional predictive features. The proposed model demonstrates the effectiveness of integrating time-series and spatial data, offering a scalable and accurate solution for improving yield forecasting and supporting efficient operations in sugarcane production.

关键词： Accuracy Three-dimensional displays Biological system modeling Production Predictive models Sugar industry Data models Long short term memory Genetic algorithms Drones

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LK-ORB-SLAM3: An RGB-D Visual SLAM Algorithm Fusing Optical Flow Tracking and Feature Matching

LK-ORB-SLAM3: An RGB-D Visual SLAM Algorithm Fusing Optical ...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Juan Dai Shengqiang Sun Jingwei Liu Zhong Su Beijing Key Laboratory of High Dynamic Navigation Technology Beijing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing China School of Automation Beijing Information Science & Technology University Beijing China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In response to the issue of poor real-time performance caused by the large computational load of feature-based methods, this paper proposed an RGB-D visual SLAM algorithm which integrates optical flow and feature-based methods. This algorithm combined optical flow with the feature-based method, effectively improving the real-time performance of the algorithm while maintaining high precision. Firstly, keyframes are matched using feature-based methods, and camera motion is computed by minimizing the reprojection error. For non-keyframes, keypoints are extracted without computing descriptors and tracked using the optical flow pyramid, and pose estimation is achieved by minimizing the photometric error. Secondly, a dual mechanism based on time and space is employed to execute keyframes insertion which ensures tracking stability. Additionally, an adaptive threshold algorithm is proposed to enhance tracking robustness in low-light environments. Experimental results on the TUM dataset indicated that, with comparable average localization accuracy, the proposed algorithm reduces average tracking cost by 37.19%, achieving real-time frame rates of up to 45 fps on mid-to-low-performance CPUs.

关键词： Location awareness Visualization Costs Accuracy Simultaneous localization and mapping Tracking Heuristic algorithms Real-time systems Optical flow Robots

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SGN: Semantic-Guided Net for 3D Semantic Segmentation in Cluttered Scenes

SGN: Semantic-Guided Net for 3D Semantic Segmentation in Clu...

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international conference on Cybernetics, robotics and Control (CRC)

作者： Zihao Wan Jianhua Hu Yunkuan Wang Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Inteligence University of Chinese Academy of Sciences Beijing China

3D semantic segmentation is a fundamental task in robotic manipulation and computer vision. However, due to the limitation of heuristic point cloud upsampling methods, the performance of 3D semantic segmentation under cluttered conditions is far from satisfactory, which severely limits the performance of robotic grasping. Specifically, for each query point, previous point-based upsampling methods only choose k nearest neighbors in 3D space as the point set for interpolation, which may cause ambiguity in predicting the category of the query point. To tackle this issue, this paper proposes a novel point cloud semantic segmentation network called Semantic-Guided Net (SGN). The key to SGN is Semantic-Guided Upsampler (SGUS). SGUS make use of both 3D position and semantic information to find the neighbors for interpolation, which reduces the number of irrelevant points from different categories. Furthermore, this paper presents a 3D semantic segmentation dataset called Semantic Cluttered Objects (SCO) to test the proposed module under robotic vision grasping circumstances. Extensive experiments on S3DIS and SCO show the effectiveness of SGN.

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