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UKRAS22 Conference "Robotics for Unconstrained Environments" Proceedings最新文献

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Failure Mode Analysis (FMA) for Visual-Based Navigation for UAVs in Urban Environment 城市环境下无人机视觉导航失效模式分析(FMA
Tarafder Elmi Tabassum, I. Petrunin, Z. Rana
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引用次数: 0
Event-based Spatio-temporal down-sampling 基于事件的时空降采样
Anindya Ghosh, T. Nowotny, James C. Knight
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引用次数: 1
Survey on Virtual Reality and Robotics 虚拟现实与机器人研究综述
J. Heselden, Jack Stevenson
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引用次数: 0
Overhead tracking and control of a mobile robot 移动机器人的空中跟踪与控制
B. Fawaz, Jack Thompson, Rosemary Brindle, W. R. Evans, Tomos Fearn, Claudio Coppola, O. Akanyeti
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引用次数: 0
Robotic Climbing of Ladder Civil Infrastructure 梯子民用基础设施的机器人攀爬
Shijia Wu, R. Richardson, B. Kaddouh
{"title":"Robotic Climbing of Ladder Civil Infrastructure","authors":"Shijia Wu, R. Richardson, B. Kaddouh","doi":"10.31256/su2rz3z","DOIUrl":"https://doi.org/10.31256/su2rz3z","url":null,"abstract":"","PeriodicalId":144066,"journal":{"name":"UKRAS22 Conference \"Robotics for Unconstrained Environments\" Proceedings","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116288181","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: 0
Effect of Head Movements on Robotic Fish Swimming Efficiency 头部运动对机器鱼游泳效率的影响
R. Sterling, O. Akanyeti
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引用次数: 0
Localising Weeds Using a Prototype Weed Sprayer 使用杂草喷雾器原型定位杂草
Madeleine Darbyshire, Adrian Salazar-Gomez, Callum Lennox, Junfeng Gao, E. Sklar, Simon Parsons
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引用次数: 1
Assistive Cyber-Physical-Social Systems in the healthcare industry 医疗保健行业的辅助网络-物理-社会系统
Peter Antai, L. Alboul, Joshua Roe
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引用次数: 1
Shared autonomy for robotic inspection 共享机器人检查自主权
Jonatan Scharff Willners, S. Xu, T. Luczynski, Sean Katagiri, Joshua Roe, Sen Wang, Y. Pétillot
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引用次数: 0
Evolving complex terrain navigation: Emergent contour following from a low-resolution sensor 复杂地形导航的进化:低分辨率传感器的紧急等高线跟踪
Dexter R. Shepherd, James Knight
—This paper investigates evolutionary approaches to enable robotic agents to learn strategies for energy-efficient navigation through complex terrain, consisting of water and different heights. Agents, equipped with a low-resolution depth sensor, must learn how to navigate between a randomly chosen start/end position in a procedurally generated world, along a path which minimises energy usage. The solution that consistently emerged, was an agent that followed the contours of the map, resulting in near-optimal performance in little evolutionary time. Further, initial experiments with a real robot and Kinect sensor showed that the simulated model successfully predicted the correct movement that would be needed to follow contours. This demonstrated both that the evolved strategies are robust to noise and capable of crossing the reality gap. We suggest that this robustness is due to the use of a low-resolution sensor.
-本文研究了进化方法,使机器人代理能够学习通过复杂地形(包括水和不同高度)的节能导航策略。装备了低分辨率深度传感器的智能体必须学会如何在程序生成世界中随机选择的开始/结束位置之间导航,并沿着最小化能量消耗的路径进行导航。不断出现的解决方案是一个遵循地图轮廓的代理,在很少的进化时间内产生近乎最佳的性能。此外,对真实机器人和Kinect传感器的初步实验表明,模拟模型成功地预测了跟随轮廓所需的正确运动。这证明了进化的策略对噪声具有鲁棒性,并且能够跨越现实差距。我们认为这种鲁棒性是由于使用了低分辨率传感器。
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引用次数: 1
期刊
UKRAS22 Conference "Robotics for Unconstrained Environments" Proceedings
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