2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)最新文献_第5页

New Approach to Supervise Localization Algorithms 监督定位算法的新方法

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) Pub Date : 2019-04-01 DOI: 10.1109/ICARSC.2019.8733650

Fabrício O. Coelho, P. Guedes, D. Guimaraes, H. Sobreira, A. Moreira

引用次数: 0

AdaptPack Studio: Automatic Offline Robot Programming Framework for Factory Environments AdaptPack Studio:用于工厂环境的自动离线机器人编程框架

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) Pub Date : 2019-04-01 DOI: 10.1109/ICARSC.2019.8733626

A. Castro, J. P. Souza, Luís F. Rocha, M. Silva

{"title":"AdaptPack Studio: Automatic Offline Robot Programming Framework for Factory Environments","authors":"A. Castro, J. P. Souza, Luís F. Rocha, M. Silva","doi":"10.1109/ICARSC.2019.8733626","DOIUrl":"https://doi.org/10.1109/ICARSC.2019.8733626","url":null,"abstract":"The brisk and dynamic environment that factories are facing, both as an internal and an external level, requires a collection of handy tools to solve emerging issues in the industry 4.0 context. Part of the common challenges that appear are related to the increasing demand for high adaptability in the organizations' production lines. Mechanical processes are becoming faster and more adjustable to the production diversity in the Fast Moving Consumer Goods (FMCG). Concerning the previous characteristics, future factories can only remain competitive and profitable if they have the ability to quickly adapt all their production resources in response to inconstant market demands. Having previous concerns in focus, this paper presents a fast and adaptative framework for automated cells modeling, simulation and offline robot programming, focused on palletizing operations. Established as an add-on for the Visual Components (VC) 3D manufacturing simulation software, the proposed application allows performing fast layout modeling and automatic offline generation of robot programs. Furthermore, A* based algorithms are used for generating collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. The software evaluation was tested inside the VC simulation world and in the real-world scenario. Results have shown to be concise and accurate, with minor displacement inaccuracies due to differences between the virtual model and the real world.","PeriodicalId":198320,"journal":{"name":"2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129501892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Trends, Challenges and Adopted Strategies in RoboCup@Home 趋势，挑战和采用的策略RoboCup@Home

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) Pub Date : 2019-03-06 DOI: 10.1109/ICARSC.2019.8733622

Mauricio Matamoros, Viktor Seib, D. Paulus

引用次数: 12

Q-Learning for Autonomous Mobile Robot Obstacle Avoidance 自主移动机器人避障的q学习方法

2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) Pub Date : 1900-01-01 DOI: 10.1109/ICARSC.2019.8733621

T. Ribeiro, Fernando Gonçalves, Ines Garcia, G. Lopes, A. Ribeiro

{"title":"Q-Learning for Autonomous Mobile Robot Obstacle Avoidance","authors":"T. Ribeiro, Fernando Gonçalves, Ines Garcia, G. Lopes, A. Ribeiro","doi":"10.1109/ICARSC.2019.8733621","DOIUrl":"https://doi.org/10.1109/ICARSC.2019.8733621","url":null,"abstract":"An approach to the problem of autonomous mobile robot obstacle avoidance using Reinforcement Learning, more precisely Q-Learning, is presented in this paper. Reinforcement Learning in Robotics has been a challenging topic for the past few years. The ability to equip a robot with a powerful enough tool to allow an autonomous discovery of an optimal behavior through trial-and-error interactions with its environment has been a reason for numerous deep research projects. In this paper, two different Q-Learning approaches are presented as well as an extensive hyperparameter study. These algorithms were developed for a simplistically simulated Bot'n Roll ONE A (Fig. 1). The simulated robot communicates with the control script via ROS. The robot must surpass three levels of iterative complexity mazes similar to the ones presented on RoboParty [1] educational event challenge. For both algorithms, an extensive hyperparameter search was taken into account by testing hundreds of simulations with different parameters. Both Q-Learning solutions develop different strategies trying to solve the three labyrinths, enhancing its learning ability as well as discovering different approaches to certain situations, and finishing the task in complex environments.","PeriodicalId":198320,"journal":{"name":"2019 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)","volume":"19 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127006955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21