具有嵌入式随机功能的贝叶斯优化技术,用于增强机器人的避障能力

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Control Engineering Practice Pub Date : 2024-10-30 DOI:10.1016/j.conengprac.2024.106141
Catalin Stefan Teodorescu, Andrew West, Barry Lennox
{"title":"具有嵌入式随机功能的贝叶斯优化技术,用于增强机器人的避障能力","authors":"Catalin Stefan Teodorescu,&nbsp;Andrew West,&nbsp;Barry Lennox","doi":"10.1016/j.conengprac.2024.106141","DOIUrl":null,"url":null,"abstract":"<div><div>Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&amp;D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"154 ","pages":"Article 106141"},"PeriodicalIF":5.4000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance\",\"authors\":\"Catalin Stefan Teodorescu,&nbsp;Andrew West,&nbsp;Barry Lennox\",\"doi\":\"10.1016/j.conengprac.2024.106141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&amp;D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.</div></div>\",\"PeriodicalId\":50615,\"journal\":{\"name\":\"Control Engineering Practice\",\"volume\":\"154 \",\"pages\":\"Article 106141\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Control Engineering Practice\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0967066124003009\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering Practice","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0967066124003009","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

设计一种能将人-机器人-环境互动的随机性纳入其中的避障算法具有挑战性。在核环境等高风险活动中,必须采用综合方法来处理不确定性。本文以机器人的安全远程操作为背景,提出了一种基于贝叶斯优化的自动迭代采样程序,训练机器人预测人类操作员的行为。具体来说,使用高斯过程回归模型来学习安全停止动作的有效表示,这是实施避障共享控制算法所必需的。然后,根据当前的实际情况,利用该模型预测未来执行安全停车动作所需的时间。控制算法希望这个值是合理的高值;如果不是,它将逐渐降低人类操作员的权限。拟议方法的一个显著特点是使用统计置信度指标作为调整参数,旨在提供是否能避开障碍物的统计指示。概念验证实验使用了三个适合核机器人技术使用的机器人平台:配备 Velodyne VLP16(三维激光雷达)的 SuperDroid HD2 水陆两用机器人、配备 Realsense D435 深度相机的 AgileX Scout Mini R&D Pro 陆地机器人,以及配备 RPLIDAR A3(二维激光雷达)的 Husarion ROSBot 2.0 Pro。测试结果表明,与穷举式网格方法相比,所提出的贝叶斯优化方法所使用的数据量减少了 8 倍,而且还能提供与机器人无关的稳健避障功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Bayesian optimization with embedded stochastic functionality for enhanced robotic obstacle avoidance
Designing an obstacle avoidance algorithm that incorporates the stochastic nature of human–robot-environment interactions is challenging. In high risk activities, such as those found in nuclear environments, a comprehensive approach towards handling uncertainty is essential. In this article, in the context of safe teleoperation of robots, an automated iterative sampling procedure based on Bayesian optimization is proposed, where the robot is trained to predict the behaviour of a human operator. Specifically, a Gaussian process regression model is used to learn an effective representation of a safe stop manoeuvre, required for implementing an obstacle avoidance shared control algorithm. This model is then used to predict the future time duration to execute a safe stop manoeuvre, given the current real-world circumstances. The control algorithm expects this value to be reasonably high; if not, it will gradually reduce the human operator’s authority. A distinctive attribute of the proposed method is the use of statistical confidence metrics as tuning parameters, intended to provide a statistical indication of whether or not an obstacle will be avoided. The proof-of-concept experiments were carried out using three robotic platforms suited for use in nuclear robotics, an amphibious SuperDroid HD2 robot equipped with a Velodyne VLP16 (a 3D lidar), an AgileX Scout Mini R&D Pro land robot fitted with a Realsense D435 depth camera, and a Husarion ROSBot 2.0 Pro supplied with an RPLIDAR A3 (a 2D lidar). The test results show that the proposed Bayesian optimization method uses 8 times less data compared to an exhaustive grid approach, and that it provides a robot-agnostic, robust obstacle avoidance.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Control Engineering Practice
Control Engineering Practice 工程技术-工程:电子与电气
CiteScore
9.20
自引率
12.20%
发文量
183
审稿时长
44 days
期刊介绍: Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.
期刊最新文献
Physical-anchored graph learning for process key indicator prediction Predictive sliding mode control for flexible spacecraft attitude tracking with multiple disturbances Spatial–temporal cooperative guidance with no-fly zones avoidance Sliding-mode energy management strategy for dual-source electric vehicles handling battery rate of change of current Optimization of the energy-comfort trade-off of HVAC systems in electric city buses based on a steady-state model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1