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Online Tool Selection with Learned Grasp Prediction models

Online Tool Selection with Learned Grasp Prediction Models

引用

IEEE international conference on robotics and automation (ICRA)

作者： Khashayar Rohanimanesh Jake Metzger William Richards Aviv Tamar Osaro Inc. San Francisco CA USA Accenture Inc. San Francisco CA USA Technion - Israel Institute of Technology Haifa Israel

Deep learning-based grasp prediction models have become an industry standard for robotic bin-picking systems. To maximize pick success, production environments are often equipped with several end-effector tools that can be swapped on-the-fly, based on the target object. Tool-change, however, takes time. Choosing the order of grasps to perform, and corresponding tool-change actions, can improve system throughput; this is the topic of our work. The main challenge in planning tool change is uncertainty - we typically cannot see objects in the bin that are currently occluded. Inspired by queuing and admission control problems, we model the problem as a Markov Decision Process (MDP), where the goal is to maximize expected throughput, and we pursue an approximate solution based on model predictive control, where at each time step we plan based only on the currently visible objects. Special to our method is the idea of void zones, which are geometrical boundaries in which an unknown object will be present, and therefore cannot be accounted for during planning. Our planning problem can be solved using integer linear programming (ILP). However, we find that an approximate solution based on sparse tree search yields near optimal performance at a fraction of the time. Another question that we explore is how to measure the performance of tool-change planning: we find that throughput alone can fail to capture delicate and smooth behavior, and propose a principled alternative. Finally, we demonstrate our algorithms on both synthetic and real world bin picking tasks.

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Industrial Camera-Aided Trajectory Planning for Robotic Welding on Reflective Surfaces

Industrial Camera-Aided Trajectory Planning for Robotic Weld...

引用

IEEE international conference on Real-time Computing and robotics (RCAR)

作者： Kristoffer Klingenberg Karl Victor Johannessen Ozan Kaya Lars Tingelstad Department of Mechanical and Industrial Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

Recent advancements in robotic welding, vision-aided control, and automation strategies have gained prominence due to their inherent advantages, such as high precision, rapid detection capabilities, and increased versatility, surpassing traditional manual welding operations. This shift towards automation is driven by the quest for efficiency and the need for enhanced safety, particularly in hazardous working conditions characterized by fumes, high temperatures, and pressures. This study considers the vision-aided trajectory generation strategies, which represent a combination of software and hardware-based approaches designed for automatic robotic welding. The proposed process involves the utilization of image processing techniques facilitated by an industrial camera (Zivid 2) to compute essential parameters. These parameters include the generation of a welding trajectory, determination of the initial welding position, and compensation for positioning errors arising from the workpiece’s placement within the working environment. Furthermore, our research addresses several challenges inherent to the implementation of a vision-aided trajectory planner, such as visual calibration, defining welding workpieces, and control of the industrial robot arm (Yaskawa GP25-12). While these aspects are pivotal, the primary focus remains on effectively compensating for misalignment in the workpiece’s position, which is crucial for achieving optimal welding outcomes. In this study, we present the proposed architecture, elucidate challenges associated with various approaches and techniques, provide kinematic models, and delve into the intricacies of the robotic system’s automatic welding framework. The objective of this research is to contribute to the ongoing discourse on the advancement of automation technologies in welding, with the aim of fostering efficiency, safety, and precision in challenging industrial environments. Additionally, the research seeks to enhance the reliability

关键词： automation Service robots Trajectory planning Welding Robot vision systems Real-time systems Trajectory

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A Configuration Measurement Method of Cable-Driven Space Manipulator by Link Equivalent Center Points Recognition

A Configuration Measurement Method of Cable-Driven Space Man...

引用

2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： Wang, Fengxu Yan, Lei Li, Junxiang Lin, Boyang Xu, Wenfu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781665481090

A cable-driven space manipulator has great application potential in space operations due to its advantages of flexible movement and slender body. However, the pose accuracy of its end-effector is worse than that of traditional space manipulators because it's difficult to measure the joint angles. The lack of shape information will increase the risk of colliding with others. However, the method of measuring configuration by adding cooperative markers on its body is complex and not suitable in space. To solve the above problems, this paper proposes a method of configuration measurement for the natural characteristics. This method solves the links' equivalent center points observed by the stereovision instead of adding cooperative markers. The kinematics model is derived to reconstruct the shape of the manipulator. The center points of links identification and kinematics modeling adopt the series-by-segment solution to greatly improve the computational efficiency and avoid the multiply solutions of redundant manipulators' inverse kinematics. A simulation system for configuration measurement of cable-driven space manipulator is established, and the above methods is verified by simulation and experiment. The simulation and experimental results show that the proposed method has high accuracy. © 2022 IEEE.

关键词： Stereo vision

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CNN-XGBoost Based Ensemble Model for Short-Term Load Forecasting

CNN-XGBoost Based Ensemble Model for Short-Term Load Forecas...

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robotics, automation, Artificial-intelligence and Internet-of-Things (RAAICON), 2019 IEEE international conference on

作者： Kazi Reyad Hossain Md Samin Safayet Islam Md Rashidul Islam Md. Rafiqul Islam Sheikh Md. Omer Faruque Department of EEE Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (数字)9798331534400

ISBN: (纸本)9798331534417

Precise short-term load forecasting is crucial in the electric power industry, which serves a vital purpose in maintaining a balance between demand and supply. Maintaining this equilibrium is essential for ensuring the stability and efficiency of power systems. This forecasting task, however, poses significant challenges due to its inherent complexity. This paper presents a new approach for short-term load forecasting through the combination of convolutional neural networks (CNN) and extreme gradient boosting (XGBoost) algorithms. The model incorporates the feature extraction skills of CNN with the sequential learning strength of XGBoost. Both the techniques are ensembled with gradient boosting, which functions as a meta-learner. Gradient boosting combines the individual predictions done by the CNN and XGBoost and enhances the accuracy. The research used the daily load consumption data of Queensland, Australia. This proposed ensemble method is evaluated on the dataset and compared with other forecasting techniques. The outcomes indicated that the suggested ensemble model surpasses the existing forecasting algorithms in terms of various evaluation metrics.

关键词： Accuracy Load forecasting Predictive models Power system stability Boosting Prediction algorithms Complexity theory Convolutional neural networks Forecasting Load modeling

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A survey of image clustering: Taxonomy and recent methods

A survey of image clustering: Taxonomy and recent methods

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2021 IEEE international conference on Real-Time Computing and robotics, RCAR 2021

作者： Liu, Jiaxin Wang, Dongwei Yu, Siquan Li, Xueliang Han, Zhi Tang, Yandong State Grid Liaoning Electric Power Research Institute Shenyang China School of Technology Beijing Forestry University Beijing China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Grid Shandong Electric Power Company Shandong China

ISBN: (纸本)9781665436786

Image clustering is a fundamental problem in computer vision domains. In this survey, we provide a comprehensive overview of image clustering. Specifically, we first discuss the applications of image clustering across various domains. Then, we summarize the common algorithms and propose a classification of image clustering. The existing methods are classified from four aspects: autoencoder based methods, subspace clustering, graph convolution network (GCN) based methods and some other clustering methods. We introduce the main research contents and existing problems of various image clustering methods. We also introduce some recent methods and summarize the experimental results. Based on our taxonomy and analysis, creating and verifying new methods is more straightforward. Finally, we propose the future opportunities in this fast developing field. © 2021 IEEE.

关键词： Convolution

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Penetration of Artificial Intelligence Techniques to Enhance the Agricultural Productivity and the Method of Farming;Opportunities and Challenges 3

Penetration of Artificial Intelligence Techniques to Enhance...

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3rd international conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Muramulla, Sai Mounika Ponugoti, Sreelekha Srinivas, O. Dhinakaran, K. Kuchipudi, Ramu Robin, C.R. Rene Symbiosis Institute of Business Management Pune India Symbiosis International Pune India Acharya Nagarjuna University Department of Zoology and Aquaculture Guntur India Rmk College of Engineering and Technology Department of Computer Science Thiruvallur India Hasanparthy India Misrimal Navajee Munoth Jain Engineering College Department of Computer Science Chennai India Chaitanya Bharathi Institute of Technology Department of Information Technology Hyderabad India Sri Sai Ram Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336238

Each nation's economic sector depends heavily on agriculture. Every day, more people live on the planet, which raises the requirement of food. For the time being, farmers' conventional methods are unable to meet the demand. As a result, new automation techniques are launched to meet these needs and give many people in this industry excellent job prospects. In every industry, including banking, robotics, agriculture, and education, artificial intelligence(AI) has emerged as one of the most crucial technologies. It is playing a very important part in the agriculture sector and changing the agriculture industry. AI protects the agricultural sector from a variety of concerns, including food safety, population expansion, climate change, and employment problems in this industry. Due to AI, the agriculture system of today has advanced to a new level. Real-time crop monitoring, production, harvesting, processing, and marketing have all been enhanced by artificial intelligence. Numerous cutting-edge computer-based systems are created to identify several significant factors, including weed identification, yield identification, crop quality, and many others. © 2023 IEEE.

关键词： Crops

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4th Annual international conference on Data Science, Machine Learning and Blockchain Technology, AICDMB 2023

4th Annual International Conference on Data Science, Machine...

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4th Annual international conference on Data Science, Machine Learning and Blockchain Technology, AICDMB 2023

ISBN: (纸本)9781032426853

The proceedings contain 50 papers. The special focus in this conference is on Data Science, Machine Learning and Blockchain Technology. The topics include: An unsupervised approach to creating a restaurant recommendation system;Classification of alzheimer’s disease using D-DEMNET framework;comparison of machine learning and deep learning methods for detection of liver abnormality;soil micronutrient detection using machine learning;a review of tracking concept drift detection in machine learning;wearable electrogastrogram perspective for healthcare applications;computer vision based home automation;Early autism detection using ML on behavioural pattern;implementation of application prototypes for human-to-computer interactions;recommendation system for anime using machine learning algorithms;predicting bitcoin price fluctuation by Twitter sentiment analysis;microarchitecture design and verification of co-processor for floating point operation;blockchain based higher education ecosystem;document verification using blockchain;blockchain-based traceability system for readymade food products;a cloud based interactive framework for emergency medical data sharing;the analysis and interpretation of higher education teachers based on student and teachers feedback;Implementing AI on microcontrollers in fog and edge architectures;abnormality detection in chest radiograph using deep learning models;a comprehensive review on hate speech recognition utilizing natural language processing and machine learning;prevalence of migraine among collegiate students in greater Noida;Indoor navigation using BLE beacons;strategic health planner and exercise suggester;perspective of deep learning strategies for analysis of 1D biomedical signals;revolution in agriculture sector using blockchain technology;customer churn prediction using ensemble learning with neural networks;Securing crime case summary and E-FIR using blockchain concept.

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Bioheuristics as Tool for Global Optimization Problem Based on Collective Behavior Model of Animals

Bioheuristics as Tool for Global Optimization Problem Based ...

引用

international Russian automation conference (RusAutoCon)

作者： Sergey Rodzin Alexander Bozhenyuk Lada Rodzina Institute of Computer Technology and Information Security Southern Federal University Taganrog Russia Institute of Computer Technology and Information Security Taganrog Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

The main purpose of the article is to develop an effective algorithm for solving applied problems of global optimization of multidimensional unimodal and multimodal functions. Such functions are encountered in issues of engineering design, image processing and computer vision, energy and energy management, data analysis and machine learning, robotics. To achieve this goal, the article proposes a computational model of the collective behavior of a group of animals and an effective algorithm for differential vector movement. The algorithm was experimentally tested on known multidimensional unimodal and multimodal functions. The results were compared with competing algorithms. The obtained results using Wilcoxon rank-sum test for independent samples showed that the results of the algorithm are statistically significant.

关键词： Machine learning algorithms Animals Computational modeling Biological system modeling Image processing Machine learning Vectors Mathematical models Particle swarm optimization Optimization

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Pose Estimation of Moving Vehicles Based on Heuristic Rules for Autonomous Driving

Pose Estimation of Moving Vehicles Based on Heuristic Rules ...

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2022 IEEE international conference on robotics and Biomimetics, ROBIO 2022

作者： He, Yuesheng Zhang, Wei Yang, Ming The Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Shenzhen Deeproute.ai Co. Ltd China

ISBN: (纸本)9781665481090

Estimating the pose information on moving vehi-cles is one of the most fundamental functions of autonomous driving for detecting and tracking moving objects. The current methods are often based on the prior information of the point cloud distribution, however, the distribution in the actual scene changes dynamically, and is easy not to meet a priori. This paper proposes a 3D pose estimation method based on heuristic rules. In a single frame point cloud, the edge length and edge visibility are used to heuristically screen the candidate orientations established by the convex hull sampling method. On consecutive frame point clouds, tracking heuristics are employed to obtain smoother orientation estimation results. The experimental results on the KITTI dataset and the data from the actual scene of vehicles show that the proposed method reduces the average object-orientation error and improves the accuracy of estimation effectively. © 2022 IEEE.

关键词： Autonomous vehicles

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Hierarchical Meta-learning-based Adaptive Controller

Hierarchical Meta-learning-based Adaptive Controller

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IEEE international conference on robotics and automation (ICRA)

作者： Fengze Xie Guanya Shi Michael O’Connell Yisong Yue Soon-Jo Chung Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We study how to design learning-based adaptive controllers that enable fast and accurate online adaptation in changing environments. In these settings, learning is typically done during an initial (offline) design phase, where the vehicle is exposed to different environmental conditions and disturbances (e.g., a drone exposed to different winds) to collect training data. Our work is motivated by the observation that real-world disturbances fall into two categories: 1) those that can be directly monitored or controlled during training, which we call "manageable"; and 2) those that cannot be directly measured or controlled (e.g., nominal model mismatch, air plate effects, and unpredictable wind), which we call "latent". Imprecise modeling of these effects can result in degraded control performance, particularly when latent disturbances continuously vary. This paper presents the Hierarchical Meta-learning-based Adaptive Controller (HMAC) to learn and adapt to such multi-source disturbances. Within HMAC, we develop two techniques: 1) Hierarchical Iterative Learning, which jointly trains representations to caption the various sources of disturbances, and 2) Smoothed Streaming Meta-Learning, which learns to capture the evolving structure of latent disturbances over time (in addition to standard meta-learning on the manageable disturbances). Experimental results demonstrate that HMAC exhibits more precise and rapid adaptation to multi-source disturbances than other adaptive controllers. 1

关键词： Metalearning Training Adaptation models Trajectory tracking Atmospheric modeling Training data Trajectory

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