Letteratura scientifica selezionata sul tema "Allocation de tâches multi-Robots (MRTA)"
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Articoli di riviste sul tema "Allocation de tâches multi-Robots (MRTA)"
Gul, Omer Melih. "Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks". Sensors 23, n. 6 (20 marzo 2023): 3284. http://dx.doi.org/10.3390/s23063284.
Testo completoLi, Ping, e Jun Yan Zhu. "The Application of Game Theory in RoboCup Soccer Game". Applied Mechanics and Materials 530-531 (febbraio 2014): 1053–57. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.1053.
Testo completoAyari, Asma, e Sadok Bouamama. "ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization". Assembly Automation 40, n. 2 (26 settembre 2019): 235–47. http://dx.doi.org/10.1108/aa-03-2019-0056.
Testo completoElfakharany, Ahmed, e Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System". Applied Sciences 11, n. 7 (24 marzo 2021): 2895. http://dx.doi.org/10.3390/app11072895.
Testo completoYuan, Ruiping, Jiangtao Dou, Juntao Li, Wei Wang e Yingfan Jiang. "Multi-robot task allocation in e-commerce RMFS based on deep reinforcement learning". Mathematical Biosciences and Engineering 20, n. 2 (2022): 1903–18. http://dx.doi.org/10.3934/mbe.2023087.
Testo completoTamali, Abderrahmane, Nourredine Amardjia e Mohammed Tamali. "Distributed and autonomous multi-robot for task allocation and collaboration using a greedy algorithm and robot operating system platform". IAES International Journal of Robotics and Automation (IJRA) 13, n. 2 (1 giugno 2024): 205. http://dx.doi.org/10.11591/ijra.v13i2.pp205-219.
Testo completoArif, Muhammad Usman, e Sajjad Haider. "A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking". ACM Transactions on Autonomous and Adaptive Systems 16, n. 1 (31 marzo 2021): 1–23. http://dx.doi.org/10.1145/3502200.
Testo completoBadreldin, Mohamed, Ahmed Hussein e Alaa Khamis. "A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation". Advances in Artificial Intelligence 2013 (12 novembre 2013): 1–11. http://dx.doi.org/10.1155/2013/256524.
Testo completoZhao, Donghui, Chenhao Yang, Tianqi Zhang, Junyou Yang e Yokoi Hiroshi. "A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care". Machines 10, n. 8 (28 luglio 2022): 622. http://dx.doi.org/10.3390/machines10080622.
Testo completoMartin, J. G., J. R. D. Frejo, R. A. García e E. F. Camacho. "Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms". Intelligent Service Robotics 14, n. 5 (novembre 2021): 707–27. http://dx.doi.org/10.1007/s11370-021-00393-4.
Testo completoTesi sul tema "Allocation de tâches multi-Robots (MRTA)"
Chakraa, Hamza. "Οptimisatiοn techniques fοr mοnitοring a high-risk industrial area by a team οf autοnοmοus mοbile rοbοts". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH29.
Testo completoThis thesis explores the development and implementation of optimisation algorithms for monitoring industrial areas using a team of autonomous mobile robots. The research focuses on Multi-Robot Task Allocation (MRTA), where a near-optimal mission plan must be generated. A novel model considering heterogeneous robots and tasks is proposed, using Genetic Algorithms (GA) and 2-Opt local search methods to solve the problem. The thesis also integrates collision avoidance strategies, which become necessary when there are many robots and tasks. A low-level local solution handles many conflict situations during the mission, which can cause delays. Therefore, a solution for this case was proposed using clustering. Furthermore, we evaluate the proposed solutions through real-world experiments including a navigation-based algorithm that addresses collision issues. The results demonstrate the value of these algorithms in optimising task allocation and path planning for autonomous mobile robots in industrial settings, paving the way for more efficient mission planning and enhanced safety in industrial environments
Koung, Daravuth. "Cooperative navigation of a fleet of mobile robots". Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0044.
Testo completoThe interest in integrating multirobot systems (MRS) into real-world applications is increasing more and more, especially for performing complex tasks. For loadcarrying tasks, various load-handling strategies have been proposed such as: pushingonly, caging, and grasping. In this thesis, we aim to use a simple handling strategy: placing the carrying object on top of a group of wheeled mobile robots. Thus, it requires a rigid formation control. A consensus algorithm is one of the two formation controllers we apply to the system. We adapt a dynamic flocking controller to be used in the singleintegrator system, and we propose an obstacle avoidance that can prevent splitting while evading the obstacles. The second formation control is based on hierarchical quadratic programming (HQP). The problem is decomposed into multiple task objectives: formation, navigation, obstacle avoidance, velocity limits. These tasks are represented by equality and inequality constraints with different levels of priority, which are solved sequentially by the HQP. Lastly, a study on task allocation algorithms (Contract Net Protocol and Tabu Search) is carried out in order to determine an appropriate solution for allocating tasks in the industrial environment
Bautin, Antoine. "Stratégie d'exploration multirobot fondée sur le calcul de champs de potentiels". Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0261/document.
Testo completoThis thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge
Bautin, Antoine. "Stratégie d'exploration multirobot fondée sur le calcul de champs de potentiels". Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0261.
Testo completoThis thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge
Capitoli di libri sul tema "Allocation de tâches multi-Robots (MRTA)"
Koubaa, Anis, Sahar Trigui e Imen Chaari. "Indoor Surveillance Application using Wireless Robots and Sensor Networks". In Mobile Ad Hoc Robots and Wireless Robotic Systems, 19–57. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2658-4.ch002.
Testo completoPal, Aritra, Anandsingh Chauhan, Mayank Baranwal e Ankush Ojha. "Optimizing Multi-Robot Task Allocation in Dynamic Environments via Heuristic-Guided Reinforcement Learning". In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240705.
Testo completoAtti di convegni sul tema "Allocation de tâches multi-Robots (MRTA)"
Agrawal, Aakriti, Senthil Hariharan, Amrit Singh Bedi e Dinesh Manocha. "DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments". In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981353.
Testo completoChuang, Ching-Wei, e Harry H. Cheng. "A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments". In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66902.
Testo completoYan, Fuhan, e Kai Di. "Multi-robot Task Allocation in the Environment with Functional Tasks". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/653.
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