Bibliografías temáticas / Multi-Robot task allocation (MRTA)

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Literatura académica sobre el tema "Multi-Robot task allocation (MRTA)"

Autor: Grafiati
Publicado: 25 de enero de 2025
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Multi-Robot task allocation (MRTA)"

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Li, Ping, and Jun Yan Zhu. "The Application of Game Theory in RoboCup Soccer Game." Applied Mechanics and Materials 530-531 (February 2014): 1053–57. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.1053.

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In response to characters of multi-robot systems in RoboCup soccer game and dependence between decisions of robots, multi-robot systems task allocation was analyzed by means of game theory in this paper. Formalized description based on game theory for multi-robot system task allocation was offered, and a game theory based task allocation algorithm for multi-robot systems (GT-MRTA) was proposed. Experiments show that GT-MRTA has low complexity, and less time-consumption, can obtain comparative schemes with centralized method, and shows good robustness to communication failure and robot failure.
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Gul, Omer Melih. "Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks." Sensors 23, no. 6 (2023): 3284. http://dx.doi.org/10.3390/s23063284.

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In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M
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Martin, Javier G., Francisco Javier Muros, José María Maestre, and Eduardo F. Camacho. "Multi-robot task allocation clustering based on game theory." Robotics and Autonomous Systems 161 (March 5, 2023): 104314. https://doi.org/10.1016/j.robot.2022.104314.

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A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the propos
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Arjun, Krishna, David Parlevliet, Hai Wang, and Amirmehdi Yazdani. "Optimizing Coalition Formation Strategies for Scalable Multi-Robot Task Allocation: A Comprehensive Survey of Methods and Mechanisms." Robotics 14, no. 7 (2025): 93. https://doi.org/10.3390/robotics14070093.

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In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stan
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Arif, Muhammad Usman, and Sajjad Haider. "A Flexible Framework for Diverse Multi-Robot Task Allocation Scenarios Including Multi-Tasking." ACM Transactions on Autonomous and Adaptive Systems 16, no. 1 (2021): 1–23. http://dx.doi.org/10.1145/3502200.

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In a multi-robot operation, multi-tasking resources are expected to simultaneously perform multiple tasks, thus, reducing the overall time/energy requirement of the operation. This paper presents a task allocation framework named Rostam that efficiently utilizes multi-tasking capable robots. Rostam uses a task clustering mechanism to form robot specific task maps. The customized maps identify tasks that can be multi-tasked by individual robots and mark them for simultaneous execution. The framework then uses an Evolutionary Algorithm along with the customized maps to make quality task allocati
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Vandana Dabass, Suman. "Navigating the Future: An In-Depth Exploration of Quantum Computing in Swarm Intelligence based Multi Robot Systems." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 56–65. https://doi.org/10.52783/jisem.v10i14s.1992.

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In recent times, Multi-Robot Systems (MRS) have garnered extensive attention for their versatility and potential to tackle diverse real-world challenges. Among the myriad problems these systems aim to resolve the Multi-Robot Task Allocation (MRTA) stands out due to its pivotal role in optimizing collective robot performance. MRTA focuses on the efficient distribution of tasks among a group of robots, with objectives often centered around minimizing operational time or maximizing efficiency. Delving into optimization-based approaches, we critically review various studies to highlight their stre
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Yuan, Ruiping, Jiangtao Dou, Juntao Li, Wei Wang, and Yingfan Jiang. "Multi-robot task allocation in e-commerce RMFS based on deep reinforcement learning." Mathematical Biosciences and Engineering 20, no. 2 (2022): 1903–18. http://dx.doi.org/10.3934/mbe.2023087.

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<abstract><p>A Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order fulfillment system where multiple robots coordinate to complete a large number of order picking tasks. The multi-robot task allocation (MRTA) problem in RMFS is complex and dynamic, and it cannot be well solved by traditional MRTA methods. This paper proposes a task allocation method for multiple mobile robots based on multi-agent deep reinforcement learning, which not only has the advantage of reinforcement learning in dealing with dynamic environment but also can solve the task allocati
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Zhao, Donghui, Chenhao Yang, Tianqi Zhang, Junyou Yang, and Yokoi Hiroshi. "A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care." Machines 10, no. 8 (2022): 622. http://dx.doi.org/10.3390/machines10080622.

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Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task a
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Martin, J. G., J. R. D. Frejo, R. A. García, and E. F. Camacho. "Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms." Intelligent Service Robotics 14, no. 5 (2021): 707–27. http://dx.doi.org/10.1007/s11370-021-00393-4.

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AbstractThe paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will m
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Tamali, Abderrahmane, Nourredine Amardjia, and 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 13, no. 2 (2024): 205–19. https://doi.org/10.11591/ijra.v13i2.pp205-219.

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Research investigations in the realm of micro-robotics often center around strategies addressing the multi-robot task allocation (MRTA) problem. Our contribution delves into the collaborative dynamics of micro-robots deployed in targeted hostile environments. Employing advanced algorithms, these robots play a crucial role in enhancing and streamlining operations within sensitive areas. We adopt a tailored GREEDY approach, strategically adjusting weight parameters in a multi-objective function that serves as a cost metric. The objective function, designed for optimization purposes, aggregates t
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Tesis sobre el tema "Multi-Robot task allocation (MRTA)"

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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.

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Cette thèse explore le développement et la mise en œuvre d’algorithmes d’optimisation pour la surveillance de zones industrielles à l’aide d’une équipe de robots mobiles autonomes. Le travail de recherche se concentre sur l’allocation de tâches multi-robots (MRTA), où un plan de mission quasi-optimal doit être généré. Un nouveau modèle prenant en compte des robots et des tâches hétérogènes est proposé, utilisant des algorithmes génétiques (GA) et une méthode de recherche locale 2-Opt pour résoudre le problème. La thèse intègre également des stratégies d’évitement des collisions, qui deviennent
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Sarker, Md Omar Faruque. "Self-regulated multi-robot task allocation." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html.

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To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published ge
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Gage, Aaron. "Multi-robot task allocation using affect." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000465.

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Schneider, E. "Mechanism selection for multi-robot task allocation." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3018369/.

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There is increasing interest in fielding multi-robot teams for applications such as search and rescue, warehouse automation, and delivery of consumer goods. Task allocation is an important problem to solve in such multi-robot settings. Given a mission that can be decomposed into discrete tasks, the Multi-Robot Task Allocation (MRTA) problem looks for an assignment of tasks to robots that ultimately results in efficient execution of the mission. There is a range of approaches to this optimisation problem, from centralised solvers to fully distributed methods that involve no explicit coordinatio
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Das, Gautham Panamoottil. "Task allocation strategies for multi-robot systems." Thesis, Ulster University, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667759.

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Sung, Cynthia Rueyi. "Data-driven task allocation for multi-robot deliveries." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84717.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 93-97).<br>In this thesis, we present a distributed task allocation system for a team of robots serving queues of tasks in an environment. We consider how historical information about such a system's performance could be used to im
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AL-Buraiki, Omar S. M. "Specialized Agents Task Allocation in Autonomous Multi-Robot Systems." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41504.

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With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders. Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversifi
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KARAMI, HOSSEIN. "Task planning and allocation for multi-agent collaborative robot systems." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1083925.

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In this thesis we address the problem of human robot interaction in industrial environments from collaboration perspective. This thesis, in particular, focuses on introducing novel frameworks for coordination of heterogeneous teams made of humans and robots that collaboratively aim to reach to a common goal. In the last decade, robots has received enormous attentions for being employed in both industrial environments and workplaces. Thin is mainly because of a number of reasons: (I) the shift of mass production industries toward autonomous industry units, (II) huge amount of financial and s
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Sun, Dali [Verfasser], and Bernhard [Akademischer Betreuer] Nebel. "Adaptive task allocation, localization and motion planning for the multi-robot system." Freiburg : Universität, 2017. http://d-nb.info/114157571X/34.

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Dutia, Dharini. "Multi-Robot Task Allocation and Scheduling with Spatio-Temporal and Energy Constraints." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1298.

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Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the st
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Capítulos de libros sobre el tema "Multi-Robot task allocation (MRTA)"

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Zitouni, Farouq, and Ramdane Maamri. "FA-SETPOWER-MRTA: A Solution for Solving the Multi-Robot Task Allocation Problem." In Computational Intelligence and Its Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89743-1_28.

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Öztürk, Savaş, and Ahmet Emin Kuzucuoğlu. "Building a Generic Simulation Model for Analyzing the Feasibility of Multi-Robot Task Allocation (MRTA) Problems." In Modelling and Simulation for Autonomous Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43890-6_6.

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Koubaa, Anis, Hachemi Bennaceur, Imen Chaari, et al. "General Background on Multi-robot Task Allocation." In Robot Path Planning and Cooperation. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77042-0_6.

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Janati, Farzam, Farzaneh Abdollahi, Saeed Shiry Ghidary, Masoumeh Jannatifar, Jacky Baltes, and Soroush Sadeghnejad. "Multi-robot Task Allocation Using Clustering Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_19.

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Schneider, Eric, Elizabeth I. Sklar, and Simon Parsons. "Mechanism Selection for Multi-Robot Task Allocation." In Towards Autonomous Robotic Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64107-2_33.

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Tuck, Victoria Marie, Pei-Wei Chen, Georgios Fainekos, et al. "SMT-Based Dynamic Multi-Robot Task Allocation." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60698-4_20.

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Huang, Chuang, Hao Zhang, and Zhuping Wang. "Task Allocation of Multi-robot Coalition Formation." In Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3998-3_22.

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Hawley, John, and Zack Butler. "Hierarchical Distributed Task Allocation for Multi-robot Exploration." In Springer Tracts in Advanced Robotics. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32723-0_32.

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Munoz, Francisco, Ashutosh Nayak, and Seokcheon Lee. "Task Allocation in Multi-robot Systems—Resource Welfare." In Engineering Applications of Social Welfare Functions. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20545-3_5.

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Aşık, Okan, and H. Levent Akın. "Effective Multi-robot Spatial Task Allocation Using Model Approximations." In RoboCup 2016: Robot World Cup XX. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68792-6_20.

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Actas de conferencias sobre el tema "Multi-Robot task allocation (MRTA)"

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Kashid, Sujeet, and Ashwin Dharmesh Kumat. "Hierarchically Decentralized Heterogeneous Multi-Robot Task Allocation System." In 2024 7th International Conference on Intelligent Robotics and Control Engineering (IRCE). IEEE, 2024. http://dx.doi.org/10.1109/irce62232.2024.10739829.

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Dhanaraj, Neel, Jeon Ho Kang, Anirban Mukherjee, Heramb Nemlekar, Stefanos Nikolaidis, and Satyandra K. Gupta. "Multi-Robot Task Allocation Under Uncertainty Via Hindsight Optimization." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611370.

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Ching-Wei, Chuang, and Lin Wei-Yu. "Task Decomposition and Multi-Robot Task Allocation in Exploration With Bayesian Networks." In 2024 Eighth IEEE International Conference on Robotic Computing (IRC). IEEE, 2024. https://doi.org/10.1109/irc63610.2024.00018.

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Lee, Goeun, Donggil Lee, and Yoonseob Lim. "Task Allocation for Heterogeneous Multi-Robot Systems with Diverse Capabilities." In 2024 24th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773254.

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Xiong, Baolin, and Chao Chen. "Multi-Strategy Improved Dung Beetle Optimization Algorithm for Multi-Robot Task Allocation." In 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC). IEEE, 2024. https://doi.org/10.1109/icairc64177.2024.10900149.

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Heppner, Georg, David Oberacker, Arne Roennau, and Rüdiger Dillmann. "Behavior Tree Capabilities for Dynamic Multi-Robot Task Allocation with Heterogeneous Robot Teams." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610515.

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Calvo, Álvaro, and Jesús Capitán. "Optimal Task Allocation for Heterogeneous Multi-robot Teams with Battery Constraints." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10611147.

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Baccouche, Chaima, Imen Iben Ammar, Dimitri Lefebvre, and Achraf Jabeur Telmoudi. "A preliminary study about multi-robot task allocation with energy constraints." In 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE). IEEE, 2024. http://dx.doi.org/10.1109/case59546.2024.10711402.

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Yan, Fuhan, and Kai Di. "Multi-robot Task Allocation in the Environment with Functional Tasks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/653.

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Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the
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Chuang, Ching-Wei, and 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.

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Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed
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Informes sobre el tema "Multi-Robot task allocation (MRTA)"

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Lerman, Kristina, Chris Jones, Aram Galstyan, and Maja J. Mataric. Analysis of Dynamic Task Allocation in Multi-Robot Systems. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada459067.

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