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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5311 条记录，以下是391-400 订阅

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Design and Development of a Research Oriented Low Cost robotics Platform with a Novel Dynamic Global Path Planning Approach 8

Design and Development of a Research Oriented Low Cost Robot...

引用

8th international conference on control, automation and robotics, ICCAR 2022

作者： Rajapakshe, Shalutha Hettiarachchi, Ramith Robotics And Autonomous Systems Group Csiro PullenvaleQLD4069 Australia University Of Moratuwa Department Of Electronic And Telecommunication Engineering Sri Lanka

ISBN: (纸本)9781665481168

Autonomous navigation systems based on computer vision sensors often require sophisticated robotics platforms which are very expensive. this poses a barrier for the implementation and testing of complex localization, mapping, and navigation algorithms that are vital in robotics applications. Addressing this issue, in this work, Robot Operating System (ROS) supported mobile robotics platforms are compared and an end-to-end implementation of an autonomous navigation system based on a low-cost educational robotics platform, AlphaBot2 is presented, while integrating the Intel RealSense D435 camera. Furthermore, a novel approach to implement dynamic path planners as global path planners in the ROS framework is presented. We evaluate the performance of this approach and highlight the improvements that could be achieved through a dynamic global path planner. this low-cost modified AlphaBot2 robotics platform along with the proposed dynamic global path planning approach will be useful for researchers and students for getting hands-on experience with computer vision-based navigation systems. © 2022 IEEE.

关键词： Robot Operating System

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Stiffness-based Offline Toolpath Error Compensation for Robotized Incremental Forming 7

Stiffness-based Offline Toolpath Error Compensation for Robo...

引用

7th international conference on automation, control and Robots, ICACR 2023

作者： Shaker, Walid K. Klimchik, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia Lincoln United Kingdom

ISBN: (纸本)9798350302882

this paper addresses the challenge of the poor accuracy and the toolpath deviation in Incremental Sheet Forming (ISF) caused by the forming forces applied to the workpiece. the paper proposes an offline toolpath error compensation approach based on stiffness analysis. A truncated cone was used as a test case and the numerical simulation of the ISF process was conducted on Abaqus CAE software to extract forming forces corresponding to toolpath points. the compensation algorithm is based on the Virtual Joint Method (VJM) and is implemented on a FANUC R-2000iC/165F robot using RoboDK API for MATLAB. the results show that the deviation of the robot tool due to the forming forces may reach up to 20 mm which can be handled by the compensated toolpath. Finally, the study proposes an overall process for the stiffness-based correction algorithm. © 2023 IEEE.

关键词： Stiffness

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Transfer-Robot Task Allocation Algorithm Considering Production Priority for Flexible Job-Shop Scheduling Problem

Transfer-Robot Task Allocation Algorithm Considering Product...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Meng Zhou Xinheng Wang Zhongxing Liang Chang Wang Jing Wang College of Electrical and Control Engineering North China University of Technology Beijing P. R. China Beijing Aerospace Automatic Control Institute Beijing China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin P. R. China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this paper proposes a flexible job-shop scheduling problem optimization method, which focuses on providing solutions for industrial production. First, in terms of model construction, the method further considers the cost of automated guided vehicles and the priority of workpiece production based on previous methods. then, this method solves the model by non-dominated sorting genetic algorithm with self-cross and delete-mutation. It reduces the production time and energy by an average of 6.4% and 19.4%, which are 5.4% and 15.1% with the priority. Finally, the simulation verifies that the method effectively reduces the production cost while realizing the adjustment of the automated guided vehicle number and the workpiece production sequence.

关键词： Hands Remotely guided vehicles Job shop scheduling Costs Optimization methods Production Resource management Vehicle dynamics Sorting Genetic algorithms

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Towards vision-Based Dual Arm Robotic Fruit Harvesting 19

Towards Vision-Based Dual Arm Robotic Fruit Harvesting

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19th IEEE international conference on automation Science and Engineering, CASE 2023

作者： Gursoy, Ege Navarro, Benjamin Cosgun, Akansel Kulic, Dana Cherubini, Andrea Univ. Montpellier Lirmm Cnrs Montpellier France Monash University Australia Deakin University Australia

ISBN: (纸本)9798350320695

Interest in agricultural robotics has increased considerably in recent years due to benefits such as improvement in productivity and labor reduction. However, current problems associated with unstructured environments make the development of robotic harvesters challenging. Most research in agricultural robotics focuses on single arm manipulation. Here, we propose a dual-arm approach. We present a dual-arm fruit harvesting robot equipped with a RGB-D camera, cutting and collecting tools. We exploit the cooperative task description to maximize the capabilities of the dual-arm robot. We designed a Hierarchical Quadratic Programming based control strategy to fulfill the set of hard constrains related to the robot and environment: robot joint limits, robot self-collisions, robot-fruit and robot-tree collisions. We combine deep learning and standard image processing algorithms to detect and track fruits as well as the tree trunk in the scene. We validate our perception methods on real-world RGB-D images and our control method on simulated experiments. © 2023 IEEE.

关键词： Fruits

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Over Two Years of Challenging Environmental Conditions for Localization: the IPLT Dataset 18

Over Two Years of Challenging Environmental Conditions for L...

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18th international conference on Informatics in control, automation and robotics (ICINCO)

作者： Bouaziz, Youssef Royer, Eric Bresson, Guillaume Dhome, Michel Univ Clermont Auvergne Inst Pascal CNRS SIGMA Clermont F-63000 Clermont Ferrand France Inst VEDECOM 23 Bis Allee Marronniers F-78000 Versailles France

ISBN: (纸本)9789897585227

this paper presents a new challenging dataset for autonomous driving applications: Institut Pascal Long-Term - IPLT - Dataset which was collected over two years and it contains, at the moment, 127 sequences and it still growing. this dataset has been captured in a parking lot where our experimental vehicle has followed the same path with slight lateral and angular deviations while we made sure to incorporate various environmental conditions caused by luminance, weather, seasonal changes.

关键词： Visual-based Navigation Computer vision for Transportation Long-term SLAM

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Obstacle Avoidance of Multiple UAVs Based on Reinforcement Learning

Obstacle Avoidance of Multiple UAVs Based on Reinforcement L...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Weiting Mo Jinna Li School of Information and Control Engineering Liaoning Petrochemical University Fushun China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Improving the obstacle avoidance capability of Unmanned Aerial Vehicles (UAVs) is crucial for maintaining their operational safety. UAVs with autonomous driving ability is the development trend of future aircraft. this paper introduces a novel UAV obstacle avoidance approach utilizing the Artificial Potential Field-Dueling Deep Q-Network (APF-Dueling DQN) method. According to the dynamic model of UAVs, the three-dimensional dynamic equation of UAVs is established, and the motion space of UAVs is constructed by combining pitch and heading angles. In order to improve the obstacle avoidance performance of UAVs, an improved Deep Reinforcement Learning (DRL) algorithm is designed, and the algorithm is used to improve the reward potential function of DQN algorithm. Simulation studies demonstrate that the APF-Dueling DQN approach surpasses the traditional DQN in performance, exhibiting robustness against local minima and yielding efficient, smooth flight paths. this underscores the efficacy of the APF-Dueling DQN in addressing UAV path planning problem.

关键词： Solid modeling Heuristic algorithms Dynamics Autonomous aerial vehicles Mathematical models Robustness Safety Collision avoidance Vehicle dynamics Robots

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From Design to Application: Emergency Maneuver control in a 1:3.33 Scaled Vehicle

From Design to Application: Emergency Maneuver Control in a ...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Amauri da Silva Junior Christian Birkner Reza Nakhaie Jazar Hormoz Marzbani CARISSMA Institute of Safety in Future Mobility (C-ISAFE University of Applied Sciences of Ingolstadt Germany School of Mechanical and Automotive Engineering Royal Melbourne Institute of Technology Melbourne Australia

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

the development of autonomous vehicles necessitates designing and testing advanced vehicle controllers that replace human drivers. Before reaching end users, these controllers must undergo rigorous experimental tests to verify their applicability and consistency with simulation data. Given the high cost of full-sized test vehicles, scaled vehicles offer a cost-effective and efficient alternative. this study evaluates state-of-the-art vehicle controllers for evasive maneuvers using a 1:3.33 scaled vehicle. the scaled vehicle is designed, constructed, and equipped with advanced hardware, including a centimeter-accurate GPS for localization and a MicroAutoBox III for executing the controllers. Experiments conducted with this scaled vehicle are compared to simulations performed in IPG-CarMaker®, demonstrating an accuracy range of 83% to 99.9% between the scaled vehicle and virtual reality simulations.

关键词： Actuators Solid modeling Accuracy Virtual reality Motors Sensors Vehicle dynamics Testing Global Positioning System Vehicles

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Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread 18th

Model-Based Optimization of Vaccination Strategies in Diff...

引用

18th international conference on Informatics in control, automation and robotics, ICINCO 2021

作者： Liu, Zonglin Omayrat, Muhammed Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Kassel Germany

ISBN: (纸本)9783031264733

this paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. the solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vaccines

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A Magnetic Field SLAM Algorithm Based on Bayesian Filtering and Gaussian Process Regression

A Magnetic Field SLAM Algorithm Based on Bayesian Filtering ...

引用

international conference on control, automation, robotics and vision (ICARCV)

作者： Adeb Magad Muhammad Emzir Control and Instrumentation Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

this study presents a method for simultaneously localizing and mapping magnetic fields (SLAM) via unscented Kalman filter (UKF) coupled with reduced-rank Gaussian process (GP) regression with the magnetic field measurement. the goal is to enhance the efficiency and precision of magnetic field-based localization in environments with spatial variations. the approach first involves breaking down the magnetic field potential into a series of basic functions. By employing Reduced-Rank GP Regression, the representation becomes more stream-lined, leading to quicker computations and decreased storage needs. then, two estimation techniques are compared: extended Kalman filter (EKF) and UKF filtering methods for estimating the states of the dynamic model. Simulation results indicate the effectiveness of the proposed methods in estimating the true dynamic states. Additionally, the proposed UKF design exhibits a slight improvement in accuracy at specific magnetic field length scales compared to the EKF approach.

关键词： Simultaneous localization and mapping Magnetic field measurement Filtering Simulation Heuristic algorithms Gaussian processes Approximation algorithms Magnetic fields Kalman filters Standards

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Federated Multi-Agent Reinforcement Learning Method for Energy Management of Microgrids

Federated Multi-Agent Reinforcement Learning Method for Ener...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Haochen Wang Xiaowen Wang Shuai Liu Qianyi Qin Liang Xu School of Control Science and Engineering Shandong University Jinan China School of Future Technology Shanghai University Shanghai China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In the past few years, energy management strategies based on multi-agent reinforcement learning (MARL) have been an active research topic. However, existing MARL algorithms require a massive amount of data, making it challenging to ensure data privacy. To address the problem, this paper investigates FL-MARL that combines federated learning (FL) with MARL. FL allows each agent to train based on local data and only share model parameters, which means that the actual data are not shared among agents. the framework is in conjunction with a MARL algorithm: independent proximal policy optimization (IPPO). the proposed algorithm has two advantages: 1) It can protect data privacy of each agent and 2) It can adapt to large-scale and decentralized data scenarios. Finally, the performance of the algorithm is verified by simulation.

关键词： Data privacy Privacy Federated learning Microgrids Reinforcement learning Real-time systems Energy management Robots Optimization Load modeling

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