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共 24757 条记录，以下是531-540 订阅

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Integration of Computer Vision and 4DOF SCARA Robot Arm for Tomato Sorting 15

Integration of Computer Vision and 4DOF SCARA Robot Arm for ...

引用

15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Tanvashi, Shilpa Kolanur, C.B. Aakash, P. Shri Muchchandi, Tejas Dhongadi, Hrithik Hirennavar, Akarsh Aralgundagi, Sanket Shreyakar, Vaibhav Kle Technological University Department of Automation and Robotics Karnataka Hubballi India

ISBN: (纸本)9798350370249

Automating fruit recognition and classification is an important computer vision application that can improve efficiency and consistency in the fruit industry. Manual sorting methods based on visual inspection are tedious, time-consuming, and prone to inconsistencies. This work presents the design and deployment of an automated tomato sorting system that integrates computer vision and robotics. The system classifies tomatoes into three maturity stages (Green, Half-ripe, Ripe) based on color using the YOLOv5 deep learning algorithm. A SCARA robot is used for picking and placing the sorted tomatoes into respective bins. The system demonstrates a fast inference time of 22.31 milliseconds for tomato detection and can sort three tomatoes of different maturity classes in approximately 1.2 seconds on average. This consistent and efficient sorting is based on objective color criteria rather than manual visual inspection. The system can help improve product quality, reduce labor costs, and increase throughput in tomato processing facilities. The approach can be extended to sorting other fruits and vegetables based on color attributes. © 2024 IEEE.

关键词： 4 DOF SCARA Robot arm automation Deep Learning Integrated System Tomato sorting

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Semantic Interpretation and Validation of Graph Attention-based Explanations for GNN models 21

Semantic Interpretation and Validation of Graph Attention-ba...

引用

21st international conference on Advanced robotics (ICAR)

作者： Panagiotaki, Efimia De Martini, Daniele Kunze, Lars Univ Oxford Cognit Robot Grp Oxford England Univ Oxford Mobile Robot Grp Oxford Robot Inst Dept Engn Sci Oxford AL USA

ISBN: (纸本)9798350342291

In this work, we propose a methodology for investigating the use of semantic attention to enhance the explainability of Graph Neural Network (GNN)-based models. Graph Deep Learning (GDL) has emerged as a promising field for tasks like scene interpretation, leveraging flexible graph structures to concisely describe complex features and relationships. As traditional explainability methods used in eXplainable AI (XAI) cannot be directly applied to such structures, graph-specific approaches are introduced. Attention has been previously employed to estimate the importance of input features in GDL, however, the fidelity of this method in generating accurate and consistent explanations has been questioned. To evaluate the validity of using attention weights as feature importance indicators, we introduce semantically-informed perturbations and correlate predicted attention weights with the accuracy of the model. Our work extends existing attentionbased graph explainability methods by analysing the divergence in the attention distributions in relation to semantically sorted feature sets and the behaviour of a GNN model, efficiently estimating feature importance. We apply our methodology on a lidar pointcloud estimation model successfully identifying key semantic classes that contribute to enhanced performance, effectively generating reliable post-hoc semantic explanations.

关键词： Attention eXplanable AI graph neural networks pose estimation

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Towards Multimodal Multitask Scene Understanding models for Indoor Mobile Agents 8

Towards Multimodal Multitask Scene Understanding Models for ...

引用

8th international conference on robotics and automation Engineering, ICRAE 2023

作者： Tsai, Yao-Hung Hubert Goh, Hanlin Farhadi, Ali Zhang, Jian Robotics Apple SeattleWA United States Machine Learning Research Apple CupertinoCA United States Computer Science and Engineering University of Washington SeattleWA United States Robotics Apple CupertinoCA United States

ISBN: (纸本)9798350327656

The perception system in personalized mobile agents requires developing indoor scene understanding models, which can understand 3D geometries, capture objectiveness, analyze human behaviors, etc. Nonetheless, this direction has not been well-explored in comparison with models for outdoor environments (e.g., the autonomous driving system that includes pedestrian prediction, car detection, traffic sign recognition, etc.). In this paper, we first discuss the main challenge: insufficient, or even no, labeled data for real-world indoor environments, and other challenges such as fusion between heterogeneous sources of information (e.g., RGB images and Lidar point clouds), modeling relationships between a diverse set of outputs (e.g., 3D object locations, depth estimation, and human poses), and computational efficiency. Then, we describe MMISM (Multi-modality input Multi-task output Indoor Scene understanding Model) to tackle the above challenges. MMISM considers RGB images as well as sparse Lidar points as inputs and 3D object detection, depth completion, human pose estimation, and semantic segmentation as output tasks. We show that MMISM performs on par or even better than single-task models;e.g., we improve the baseline 3D object detection results by 11.7% on the benchmark ARKitScenes dataset. © 2023 IEEE.

关键词： Object detection

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Bidirectional Communication Control for Human-Robot Collaboration 39

Bidirectional Communication Control for Human-Robot Collabor...

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IEEE international conference on robotics and automation (ICRA)

作者： Ferrari, Davide Benzi, Federico Secchi, Cristian Univ Modena & Reggio Emilia Dept Sci & Methods Engn Modena Italy

ISBN: (纸本)9781728196817

A fruitful collaboration is based on the mutual knowledge of each other skills and on the possibility of communicating their own limits and proposing alternatives to adapt the execution of a task to the capabilities of the collaborators. This paper aims at reproducing such a scenario in a human-robot collaboration setting by proposing a novel communication control architecture. Exploiting control barrier functions, the robot is made aware of its (dynamic) skills and limits and, thanks to a local predictor, it is able to assess if it is possible to execute a requested task and, if not, to propose alternative by relaxing some constraints. The controller is interfaced with a communication infrastructure that enables human and robot to set up a bidirectional communication about the task to execute and the human to take an informed decision on the behavior of the robot. A comparative experimental validation is proposed.

关键词： automation Collaboration Bidirectional control Behavioral sciences Task analysis Robots

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Performance Analysis of Deepfake Text Detection Techniques on Social-media 2

Performance Analysis of Deepfake Text Detection Techniques o...

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2nd international conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Gaur, Adamya Singh, Sanjay Kumar Saxena, Pranshu University School of Automation & Robotics Guru Gobind Singh Indraprastha University Delhi India Abes Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350382952

In the last few years, there has been remarkable progress in the domain of natural language generation & understanding. This has led to the development of enhanced text generation capability of machines that can generate spurious text or deepfake text. The technology may be exploited to change and shape public sentiment on social media. Therefore, mechanisms to detect deepfake text is a crucial task. The study emphasizes on detection of deepfake text on social media platform X (formerly Twitter). It presents the performance of machine learning classifiers Logistic regression (LR), XGB classifier, and AdaBoost classifier, utilizing feature engineering methods Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF). Deep learning methods such as Dense Neural Networks (DNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Convolutional Neural Networks (CNN)in addition to state-of-the-art models and variants such as Bi-Directional Encoder Representations from Transformers (BERT), DistilBERT & RoBERTa are also a part of study, leading to 89.17% accuracy in deepfake text detection. © 2024 IEEE.

关键词： Long short-term memory

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Automated Monitoring of Pollinators With Agricultural Robots 8

Automated Monitoring of Pollinators With Agricultural Robots

引用

8th international conference on automation, robotics and Applications (ICARA)

作者： Abdel-Raziq, Haron Petersen, Kirstin Cornell Univ Dept Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9781665483834

With a growing population, global food demand continues to rise. Many crops depend on wild and managed pollinators that are experiencing steep population declines. This directly impacts growers' ability to increase food production and is compounded by a lack of systems for monitoring and understanding pollinator behavior. Here, we present task allocation methods that would allow us to leverage existing agricultural robots to monitor both natural and wild pollinator behavior. This would supply growers with information key to improving orchard management such as the interaction between foragers and the orchard as well as the effect of foragers on crop growth. We compare three different task allocation methods for visual monitoring of pollinators designed around the type of activities necessary for an agricultural robot. Our approach is tested in a comprehensive simulator that considers the structure and bloom of an orchard and the time-evolving nature of honey bee flights. Our results indicate that intermittently monitoring an orchard permits estimations with a small (3-9 out of 25) pollinator error margin and that this strategy can also be used to detect spontaneous events.

关键词： social agents agricultural automation robotics in agriculture

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Reusage of Extended Digital Twins from Virtual Commissioning for Dynamics-Based Trajectory Planning in CNC Controlled robotics 33rd

Reusage of Extended Digital Twins from Virtual Commissioning...

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33rd international conference on Flexible automation and Intelligent Manufacturing

作者： Pfeifer, Denis Fauser, Simon Scheifele, Christian Fehr, Jorg Inst Engn & Computat Mech D-70569 Stuttgart Germany ISG Ind Steuerungstechnik GmbH D-70563 Stuttgart Germany

ISBN: (纸本)9783031744815;9783031744822

Industrial robots can be controlled by a CNC controller instead of the native robot controller. This approach provides several advantages, including robot operation synchronous with the machine tools and facilitated automation applications. However, CNC control of robots also has drawbacks compared to the robot controller: Its control technology is less advanced and implementing solutions through available interfaces is difficult due to the required expertise from different engineering domains. The primary novelty of this paper is to offer a solution for this by using digital twins (DT) from virtual commissioning (VC) with extended dynamic behaviour to improve the trajectory planning of CNC-based control. An interface between the DT platform and the CNC is developed to communicate acceleration limits based on the dynamic capabilities of the robot while the DT ensures an easily applicable method for different robot models. This method is validated in a VC simulation and on a real robot.

关键词： Digital Twin Virtual Commissioning CNC Control Trajectory Planning

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Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments 39

Non-Gaussian Risk Bounded Trajectory Optimization for Stocha...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Han, Weiqiao Jasour, Ashkan Williams, Brian MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

We address the risk bounded trajectory optimization problem of stochastic nonlinear robotic systems. More precisely, we consider the motion planning problem in which the robot has stochastic nonlinear dynamics and uncertain initial locations, and the environment contains multiple dynamic uncertain obstacles with arbitrary probabilistic distributions. The goal is to plan a sequence of control inputs for the robot to navigate to the target while bounding the probability of colliding with obstacles. Existing approaches to address risk bounded trajectory optimization problems are limited to particular classes of models and uncertainties such as Gaussian linear problems. In this paper, we deal with stochastic nonlinear models, nonlinear safety constraints, and arbitrary probabilistic uncertainties, the most general setting ever considered. To address the risk bounded trajectory optimization problem, we first formulate the problem as an optimization problem with stochastic dynamics equations and chance constraints. We then convert probabilistic constraints and stochastic dynamics constraints on random variables into a set of deterministic constraints on the moments of state probability distributions. Finally, we solve the resulting deterministic optimization problem using nonlinear optimization solvers and get a sequence of control inputs. To our best knowledge, it is the first time that the motion planning problem to such a general extent is considered and solved. To illustrate the performance of the proposed method, we provide several robotics examples.

关键词： Uncertainty Trajectory planning System dynamics Dynamics Stochastic processes Probabilistic logic Mathematical models

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FRobs_RL: A Flexible robotics Reinforcement Learning Library 18

FRobs_RL: A Flexible Robotics Reinforcement Learning Library

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IEEE 18th international conference on automation Science and Engineering (IEEE CASE)

作者： Manuel Fajardo, Jose Gonzalez Roldan, Felipe Realpe, Sebastian Hernandez, Juan D. Ji, Ze Cardenas, Pedro-F Univ Nacl Colombia Mech & Mechatron Dept Bogota Colombia Cardiff Univ Sch Comp Sci & Informat Cardiff Wales Cardiff Univ Sch Engn Cardiff Wales

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs _RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs _RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs _RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks;one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub.

关键词： Deep Learning methods Reinforcement Learning Software Architecture for Robotic and automation

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Maximum Entropy Differential Dynamic Programming 39

Maximum Entropy Differential Dynamic Programming

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IEEE international conference on robotics and automation (ICRA)

作者： So, Oswin Wang, Ziyi Theodorou, Evangelos A. Georgia Inst Technol Autonomous Control & Decis Syst Lab Atlanta GA 30332 USA

ISBN: (纸本)9781728196817

In this paper, we present a novel maximum entropy formulation of the Differential Dynamic Programming algorithm and derive two variants using unimodal and multimodal value functions parameterizations. By combining the maximum entropy Bellman equations with a particular approximation of the cost function, we are able to obtain a new formulation of Differential Dynamic Programming which is able to escape from local minima via exploration with a multimodal policy. To demonstrate the efficacy of the proposed algorithm, we provide experimental results using four systems on tasks that are represented by cost functions with multiple local minima and compare them against vanilla Differential Dynamic Programming. Furthermore, we discuss connections with previous work on the linearly solvable stochastic control framework and its extensions in relation to compositionality. Link to Video.

关键词： Heuristic algorithms Stochastic processes Cost function Approximation algorithms Prediction algorithms Entropy Mathematical models

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