Výsledky vyhledávání - "Kidnapped robot problem"

Akademický článek

Hippocampal formation-inspired global self-localization: quick recovery from the kidnapped robot problem from an egocentric perspective

Autor: Takeshi Nakashima, Shunsuke Otake, Akira Taniguchi, Katsuyoshi Maeyama, Lotfi El Hafi, Tadahiro Taniguchi, Hiroshi Yamakawa

Publikováno v: Frontiers in Computational Neuroscience, Vol 18 (2024)

It remains difficult for mobile robots to continue accurate self-localization when they are suddenly teleported to a location that is different from their beliefs during navigation. Incorporating insights from neuroscience into developing a spatial c

Externí odkaz: https://doaj.org/article/2ea59904489a4f038ee08423bbe0fb63

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Akademický článek

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Akademický článek

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Robust Hybrid Interval-Probabilistic Approach for the Kidnapped Robot Problem

Autor: Mathias Mantelli, Renata Neuland, Edson Prestes, Mariana Kolberg, Bernardo Hummes, Luc Jaulin, Renan Maffei

Publikováno v: International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific Publishing, 2021, 29 (02), pp.313-331. ⟨10.1142/S0218488521500141⟩

International audience; For a mobile robot to operate in its environment it is crucial to determine its position with respect to an external reference frame using noisy sensor readings. A scenario in which the robot is moved to another position durin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b64486b86c6e2a02f575a999bb9fa25
https://hal-ensta-bretagne.archives-ouvertes.fr/hal-03254742

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An Efficient and Robust Approach to Solve the Kidnapped Robot Problem Considering Time Variation

Autor: Liquan Jiang, Jie Meng, Gen Li, Chao Liu, Shuting Wang, Yuanlong Xie, Sun Haodong

Publikováno v: TENCON

This paper proposes an efficient and robust localization recovery method considering time variation for Monte Carlo localization (MCL), which can handle the kidnapped robot problem (KRP) with improved recovery speed and success rate. The presence of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b88c2e089434efbc153cf6d4208bb039
https://doi.org/10.1109/tencon50793.2020.9293778

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Monte Carlo-Based Localization for Kidnapped Robot Problem

Autor: Wilbert G. Aguilar, Marco Calderón, Alexis Carrera, Darwin Merizalde

Publikováno v: Smart Innovation, Systems and Technologies ISBN: 9789811548741

In this paper, we propose an algorithm for kidnapped robot problem based on Monte Carlo localization in known map using laser 360°, and initial sample in all global localization. We present performance y real-time video results. Our proposal is eval

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::0505825b7f3fb3496fe0271224697458
https://doi.org/10.1007/978-981-15-4875-8_9

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Akademický článek

Detection of kidnapped robot problem in Monte Carlo localization based on the natural displacement of the robot

Autor: Iksan Bukhori, Zool Hilmi Ismail

Publikováno v: International Journal of Advanced Robotic Systems, Vol 14 (2017)

This article proposes a new method to detect the kidnapped robot problem event in Monte Carlo localization. The method is designed in such a manner that it can provide accurate detection across all time instances, whether the robot can still recogniz

Externí odkaz: https://doaj.org/article/b4c0ee9cf2284abd8512e7e6b98139b8

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Conference

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Towards Resolving the Kidnapped Robot Problem: Topological Localization from Crowdsourcing and Georeferenced Images

Autor: Sotirios Ch. Diamantas

Publikováno v: Advances in Intelligent Systems and Computing ISBN: 9783030177942
CVC (1)

In this research, we address the kidnapped robot problem, a fundamental localization problem where a robot has been carried to an arbitrary unknown location for which no prior maps exist. In our approach, topological maps are created using a single s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d2ee01a4f4da2cbc6f71e9108ea3de8a
https://doi.org/10.1007/978-3-030-17795-9_40

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