Thematische Bibliographien / NAMO : Navigation Among Movable Obstacles

Auswahl der wissenschaftlichen Literatur zum Thema „NAMO : Navigation Among Movable Obstacles“

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Veröffentlicht am 22. Juni 2024

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Zeitschriftenartikel zum Thema "NAMO : Navigation Among Movable Obstacles"

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STILMAN, MIKE, und JAMES J. KUFFNER. „NAVIGATION AMONG MOVABLE OBSTACLES: REAL-TIME REASONING IN COMPLEX ENVIRONMENTS“. International Journal of Humanoid Robotics 02, Nr. 04 (Dezember 2005): 479–503. http://dx.doi.org/10.1142/s0219843605000545.

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In this paper, we address the problem of Navigation Among Movable Obstacles (NAMO): a practical extension to navigation for humanoids and other dexterous mobile robots. The robot is permitted to reconfigure the environment by moving obstacles and clearing free space for a path. This paper presents a resolution complete planner for a subclass of NAMO problems. Our planner takes advantage of the navigational structure through state-space decomposition and heuristic search. The planning complexity is reduced to the difficulty of the specific navigation task, rather than the dimensionality of the multi-object domain. We demonstrate real-time results for spaces that contain large numbers of movable obstacles. We also present a practical framework for single-agent search that can be used in algorithmic reasoning about this domain.
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Moghaddam, Shokraneh K., und Ellips Masehian. „Planning Robot Navigation among Movable Obstacles (NAMO) through a Recursive Approach“. Journal of Intelligent & Robotic Systems 83, Nr. 3-4 (10.02.2016): 603–34. http://dx.doi.org/10.1007/s10846-016-0344-1.

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Stilman, Mike, Koichi Nishiwaki, Satoshi Kagami und James J. Kuffner. „Planning and executing navigation among movable obstacles“. Advanced Robotics 21, Nr. 14 (Januar 2007): 1617–34. http://dx.doi.org/10.1163/156855307782227408.

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Ellis, Kirsty, Denis Hadjivelichkov, Valerio Modugno, Danail Stoyanov und Dimitrios Kanoulas. „Navigation Among Movable Obstacles via Multi-Object Pushing into Storage Zones“. IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2022.3233765.

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Huang, Ching-I., Sun-Fu Chou, Li-Wei Liou, Nathan Alan Moy, Chi-Ruei Wang, Hsueh-Cheng Wang, Charles Ahn, Chun-Ting Huang und Lap-Fai Yu. „An Evaluation Framework of Human-Robot Teaming for Navigation among Movable Obstacles via Virtual Reality-based Interactions“. IEEE Robotics and Automation Letters, 2024, 1–8. http://dx.doi.org/10.1109/lra.2024.3362138.

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Dissertationen zum Thema "NAMO : Navigation Among Movable Obstacles"

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Djerroud, Halim. „Architecture robotique pour la navigation parmi les obstacles amovibles pour un robot mobile“. Electronic Thesis or Diss., Paris 8, 2021. http://www.theses.fr/2021PA080050.

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Dans cette thèse, nous abordons la navigation autonome d'un robot mobile en milieu domiciliaire congestionné. Cette problématique relève du domaine de la navigation parmi les obstacles amovibles. Nous proposons une architecture robotique permettant la navigation parmi des obstacles fixes, amovibles et interactifs. L'objectif du robot est de rejoindre une position, tout en évitant les obstacles fixes, déplacer les obstacles amovibles s'ils gênent le passage ou bien demander à des obstacles interactifs (humain, robots, etc.) de céder le passage.Dans notre première contribution, nous proposons une architecture robotique hiérarchique baptisée VICA (VIcarious Cognitive Architecture), dont le niveau décisionnel est couplé à une architecture cognitive. Nous nous sommes inspiré des travaux sur la simplixité de Alain Berthoz qui décrivent comment le vivant prépare l'action et anticipe les réactions. L'architecture robotique se compose d'un planificateur global permettant la navigation dans un environnement inconnu et d'un planificateur local dédié à la gestion des obstacles.La seconde met en œuvre un planificateur global dont le but est de rapprocher autant que possible le robot de son objectif, en utilisant l’algorithme H* que nous avons développé.La troisième propose un planificateur local pour la gestion des obstacles. La solution proposée consiste à utiliser la simulation multi-agents dans le but d'anticiper le comportement des obstacles.L'implémentation de cette solution est réalisée dans l'architecture VICA développée sous ROS (Robot Operating System). En parallèle, nous avons développé un robot expérimental pour valider nos résultats
In this thesis, we address the autonomous navigation of a mobile robot in a congested indoor environment. This problem is related to navigation among movable obstacles (NAMO). We propose a robotic architecture allowing navigation among: fixed, removable and interactive obstacles. The objective of the robot is to reach a position, while avoiding fixed obstacles, to move removable obstacles if they obstruct the path or to ask interactive obstacles (human, robots, etc.) to give way.In our first contribution, we propose a hierarchical robotic architecture named VICA (VIcarious Cognitive Architecture), whose decisional level is coupled to a cognitive architecture. We are inspired by Alain Berthoz's work on simplexity, which describes how living organisms prepare actions and anticipate reactions. The robotic architecture is composed of a global planner allowing navigation in an unknown environment and a local planner dedicated to obstacle management.The second one implements a global planner whose goal is to bring the robot as close as possible to its goal, using the H* algorithm we have developed.The third one proposes a local planner for obstacle management. The proposed solution consists in using multi-agent simulation in order to anticipate the behavior of obstacles.The implementation of this solution is realized in the VICA architecture developed under ROS (Robot Operating System). In parallel, we have developed an experimental robot to validate our results
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Levihn, Martin. „Navigation among movable obstacles in unknown environments“. Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39559.

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This work presents a new class of algorithms that extend the domain of Navigation Among Movable Obstacles (NAMO) to unknown environments. Efficient real-time algorithms for solving NAMO problems even when no initial environment information is available to the robot are presented and validated. The algorithms yield optimal solutions and are evaluated for real-time performance on a series of simulated domains with more than 70 obstacles. In contrast to previous NAMO algorithms that required a pre-specified environment model, this work considers the realistic domain where the robot is limited by its sensor range. It must navigate to a goal position in an environment of static and movable objects. The robot can move objects if the goal cannot be reached or if moving the object significantly shortens the path. The robot gains information about the world by bringing distant objects into its sensor range. The first practical planner for this exponentially complex domain is presented. The planner reduces the search-space through a collection of techniques, such as upper bound calculations and the maintenance of sorted lists with underestimates. Further, the algorithm is only considering manipulation actions if these actions are creating a new opening in the environment. In the addition to the evaluation of the planner itself is each of this techniques also validated independently.
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Buchteile zum Thema "NAMO : Navigation Among Movable Obstacles"

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Renault, Benoit, Jacques Saraydaryan und Olivier Simonin. „Towards S-NAMO: Socially-Aware Navigation Among Movable Obstacles“. In RoboCup 2019: Robot World Cup XXIII, 241–54. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35699-6_19.

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Levihn, Martin, Jonathan Scholz und Mike Stilman. „Hierarchical Decision Theoretic Planning for Navigation Among Movable Obstacles“. In Springer Tracts in Advanced Robotics, 19–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36279-8_2.

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Konferenzberichte zum Thema "NAMO : Navigation Among Movable Obstacles"

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Renault, Benoit, Jacques Saraydaryan und and Olivier Simonin. „Modeling a Social Placement Cost to Extend Navigation Among Movable Obstacles (NAMO) Algorithms“. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9340892.

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Muguira-Iturralde, Jose, Aidan Curtis, Yilun Du, Leslie Pack Kaelbling und Tomás Lozano-Pérez. „Visibility-Aware Navigation Among Movable Obstacles“. In 2023 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023. http://dx.doi.org/10.1109/icra48891.2023.10160865.

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Stilman, Mike, Koichi Nishiwaki, Satoshi Kagami und James Kuffner. „Planning and Executing Navigation Among Movable Obstacles“. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.281731.

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Hai-Ning Wu, M. Levihn und M. Stilman. „Navigation Among Movable Obstacles in unknown environments“. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5649744.

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Scholz, Jonathan, Nehchal Jindal, Martin Levihn, Charles L. Isbell und Henrik I. Christensen. „Navigation Among Movable Obstacles with learned dynamic constraints“. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. http://dx.doi.org/10.1109/iros.2016.7759546.

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Wang, Maozhen, Rui Luo, Aykut Ozgun Onol und Taskin Padir. „Affordance-Based Mobile Robot Navigation Among Movable Obstacles“. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341337.

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Levihn, Martin, Mike Stilman und Henrik Christensen. „Locally optimal navigation among movable obstacles in unknown environments“. In 2014 IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids 2014). IEEE, 2014. http://dx.doi.org/10.1109/humanoids.2014.7041342.

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Sun, Nico, Erfu Yang, Jonathan Corney, Yi Chen und Zeli Ma. „Semantic enhanced navigation among movable obstacles in the home environment“. In 2nd UK-RAS ROBOTICS AND AUTONOMOUS SYSTEMS CONFERENCE, Loughborough, 2019. UK-RAS Network, 2019. http://dx.doi.org/10.31256/ukras19.18.

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Mueggler, Elias, Matthias Faessler, Flavio Fontana und Davide Scaramuzza. „Aerial-guided navigation of a ground robot among movable obstacles“. In 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2014. http://dx.doi.org/10.1109/ssrr.2014.7017662.

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Ellis, Kirsty, Henry Zhang, Danail Stoyanov und Dimitrios Kanoulas. „Navigation Among Movable Obstacles with Object Localization using Photorealistic Simulation“. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981587.

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