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Artykuły w czasopismach na temat „Multi-Robot task allocation (MRTA)”

Autor: Grafiati

Data publikacji: 25 stycznia 2025

Data aktualizacji: 31 lipca 2025

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1

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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Elfakharany, Ahmed, and Zool Hilmi Ismail. "End-to-End Deep Reinforcement Learning for Decentralized Task Allocation and Navigation for a Multi-Robot System." Applied Sciences 11, no. 7 (2021): 2895. http://dx.doi.org/10.3390/app11072895.

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In this paper, we present a novel deep reinforcement learning (DRL) based method that is used to perform multi-robot task allocation (MRTA) and navigation in an end-to-end fashion. The policy operates in a decentralized manner mapping raw sensor measurements to the robot’s steering commands without the need to construct a map of the environment. We also present a new metric called the Task Allocation Index (TAI), which measures the performance of a method that performs MRTA and navigation from end-to-end in performing MRTA. The policy was trained on a simulated gazebo environment. The centrali

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Hong, Le, Weicheng Cui, and Hao Chen. "A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics." Journal of Marine Science and Engineering 9, no. 8 (2021): 879. http://dx.doi.org/10.3390/jmse9080879.

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As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to

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13

Zhang, Zhenqiang, Sile Ma, and Xiangyuan Jiang. "Research on Multi-Objective Multi-Robot Task Allocation by Lin–Kernighan–Helsgaun Guided Evolutionary Algorithms." Mathematics 10, no. 24 (2022): 4714. http://dx.doi.org/10.3390/math10244714.

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Multi-robot task allocation (MRTA) and route planning are crucial for a large-scale multi-robot system. In this paper, the problem is formulated to minimize the total energy consumption and overall task completion time simultaneously, with some constraints taken into consideration. To represent a solution, a novel one-chromosome representation technique is proposed, which eases the consequent genetic operations and the construction of the cost matrix. Lin–Kernighan–Helsgaun (LKH), a highly efficient sub-tour planner, is employed to generate prophet generation beforehand as well as guide the ev

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Gautier, Paul, and Johann Laurent. "DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)." International Journal of Robotic Computing 3, no. 1 (2021): 69–98. http://dx.doi.org/10.35708/rc1870-126266.

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Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities

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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 (IJRA) 13, no. 2 (2024): 205. http://dx.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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Badreldin, Mohamed, Ahmed Hussein, and Alaa Khamis. "A Comparative Study between Optimization and Market-Based Approaches to Multi-Robot Task Allocation." Advances in Artificial Intelligence 2013 (November 12, 2013): 1–11. http://dx.doi.org/10.1155/2013/256524.

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This paper presents a comparative study between optimization-based and market-based approaches used for solving the Multirobot task allocation (MRTA) problem that arises in the context of multirobot systems (MRS). The two proposed approaches are used to find the optimal allocation of a number of heterogeneous robots to a number of heterogeneous tasks. The two approaches were extensively tested over a number of test scenarios in order to test their capability of handling complex heavily constrained MRS applications that include extended number of tasks and robots. Finally, a comparative study i

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Abderrahmane, Tamali, Tamali Mohammed, and Amardjia Nourredine. "An adaptive genetic algorithm for the optimization of multi-mobile robot collaboration." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e6270. http://dx.doi.org/10.54021/seesv5n2-064.

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The proposed approach utilizes the Robot Operating System (ROS) to simulate multi-robot collaboration across various scenarios, ensuring rigorous testing and validation of the algorithm. Our simulation environment encompasses complex tasks such as 3D digitalization, which demand precise coordination and efficient resource management among robots. The adaptive genetic algorithm (GA) continuously adjusts its parameters to improve performance, making it highly suitable for dynamic and unpredictable environments. Our results demonstrate that the adaptive GA significantly enhances the efficiency an

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Zitouni, Farouq, Ramdane Maamri, and Saad Harous. "FA–QABC–MRTA: a solution for solving the multi-robot task allocation problem." Intelligent Service Robotics 12, no. 4 (2019): 407–18. http://dx.doi.org/10.1007/s11370-019-00291-w.

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Ayari, Asma, and Sadok Bouamama. "ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization." Assembly Automation 40, no. 2 (2019): 235–47. http://dx.doi.org/10.1108/aa-03-2019-0056.

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Purpose The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on

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Kalempa, Vivian Cremer, Luis Piardi, Marcelo Limeira, and André Schneider de Oliveira. "Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories." Sensors 21, no. 19 (2021): 6536. http://dx.doi.org/10.3390/s21196536.

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This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed

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Baroudi, Uthman, Mohammad Alshaboti, Anis Koubaa, and Sahar Trigui. "Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation." Applied Sciences 10, no. 9 (2020): 3264. http://dx.doi.org/10.3390/app10093264.

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In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation

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Yuan, Ruiping, Juntao Li, Xiaolin Wang, and Liyan He. "Multirobot Task Allocation in e-Commerce Robotic Mobile Fulfillment Systems." Mathematical Problems in Engineering 2021 (October 29, 2021): 1–10. http://dx.doi.org/10.1155/2021/6308950.

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Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order picking system and has become the development trend of e-commerce logistics distribution centers. There are usually a large number of tasks need to be allocated to many robots and the picking time for e-commerce orders is usually very tight, which puts forward higher requirements for the efficiency of multirobot task allocation (MRTA) in e-commerce RMFS. Current researches on MRTA in RMFS seldom consider task correlation and the balance among picking stations. In this paper, a task time cost model considering task

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Zhang, Huiying, Yule Sun, and Fengzhi Zheng. "Research on Real-Time Multi-Robot Task Allocation Method Based on Monte Carlo Tree Search." Electronics 13, no. 24 (2024): 4943. https://doi.org/10.3390/electronics13244943.

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Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of robot operations. Traditional static task allocation strategies often struggle to meet the efficiency and responsiveness demands of these complex settings, while optimization heuristics, though improving planning time, exhibit limited scalability. To address these limi

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Choi, Byoungho, Minkyu Kim, and Heungseob Kim. "An Optimization Framework for Allocating and Scheduling Multiple Tasks of Multiple Logistics Robots." Mathematics 13, no. 11 (2025): 1770. https://doi.org/10.3390/math13111770.

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This study addresses the multi-robot task allocation (MRTA) problem for logistics robots operating in zone-picking warehouse environments. With the rapid growth of e-commerce and the Fourth Industrial Revolution, logistics robots are increasingly deployed to manage high-volume order fulfillment. However, efficiently assigning tasks to multiple robots is a complex and computationally intensive problem. To address this, we propose a five-step optimization framework that reduces computation time while maintaining practical applicability. The first step calculates and stores distances and paths be

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Irawan, Addie, Mohammad Fadhil Abas, and Nurulfadzilah Hasan. "Robot Local Network Using TQS Protocol for Land-to-Underwater Communications." Journal of Telecommunications and Information Technology 1 (March 29, 2019): 23–30. http://dx.doi.org/10.26636/jtit.2019.125818.

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This paper presents a model and an analysis of the Tag QoS switching (TQS) protocol proposed for heterogeneous robots operating in diﬀerent environments. Collaborative control is topic that is widely discussed in multirobot task allocation (MRTA) – an area which includes establishing network communication between each of the connected robots. Therefore, this research focuses on classifying, prioritizing and analyzing performance of the robot local network (RLN) model which comprises a point-to-point topology network between robot peers (nodes) in the air, on land, and under water. The proposed

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Dasgupta, Prithviraj, José Baca, K. R. Guruprasad, Angélica Muñoz-Meléndez, and Janyl Jumadinova. "The COMRADE System for Multirobot Autonomous Landmine Detection in Postconflict Regions." Journal of Robotics 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/921370.

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We consider the problem of autonomous landmine detection using a team of mobile robots. Previous research on robotic landmine detection mostly employs a single robot equipped with a landmine detection sensor to detect landmines. We envisage that the quality of landmine detection can be significantly improved if multiple robots are coordinated to detect landmines in a cooperative manner by incrementally fusing the landmine-related sensor information they collect and then use that information to visit locations of potential landmines. Towards this objective, we describe a multirobot system calle

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Ezercan Kayır, H. Hilal. "EXPERIENCED TASK-BASED MULTI ROBOT TASK ALLOCATION." ANADOLU UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY A - Applied Sciences and Engineering 18, no. 4 (2017): 864–75. http://dx.doi.org/10.18038/aubtda.340101.

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You, Jiangwei, Jianfang Jia, Xiaoqiong Pang, Jie Wen, Yuanhao Shi, and Jianchao Zeng. "A Novel Multi-Robot Task Assignment Scheme Based on a Multi-Angle K-Means Clustering Algorithm and a Two-Stage Load-Balancing Strategy." Electronics 12, no. 18 (2023): 3842. http://dx.doi.org/10.3390/electronics12183842.

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A practical task assignment is one of the core issues of a multi-robot system. In this paper, a multi-robot task assignment strategy based on load balancing is proposed to effectively balance and plan out the execution cost of each robot when it has a large number of working task points. Considering the variability of the execution task cost in practical situations with different task point categories, the multi-robot task assignment (MRTA) problem is transformed into a multiple traveling salesman problem (MTSP) using a multi-angle K-means clustering algorithm. To solve the problem of unbalanc

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Gao, Ping-an, and Zi-xing Cai. "Multi-robot task allocation for exploration." Journal of Central South University of Technology 13, no. 5 (2006): 548–51. http://dx.doi.org/10.1007/s11771-006-0085-6.

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N., Seenu, Kuppan Chetty R.M., Ramya M.M., and Mukund Nilakantan Janardhanan. "Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems." Industrial Robot: the international journal of robotics research and application 47, no. 6 (2020): 929–42. http://dx.doi.org/10.1108/ir-04-2020-0073.

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Purpose This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods. Design/methodology/approach This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establi

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CHOUDHURY, B. B., and B. B. BISWAL. "ALTERNATIVE METHODS FOR MULTI-ROBOT TASK ALLOCATION." Journal of Advanced Manufacturing Systems 08, no. 02 (2009): 163–76. http://dx.doi.org/10.1142/s0219686709001717.

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One of the most important aspects in the design of multi-robot systems (MRS) is the allocation of tasks among the robots in a productive and efficient manner. This paper presents an empirical study on task allocation strategies in multirobot environment. In general, optimal solutions are found through an exhaustive search, but because there are n × m ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible with increased number of tasks. Task allocation methodologies for multirobot systems are developed by considering their capability in terms of time and s

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Zitouni, Farouq, Ramdane Maamri, and Saad Harous. "Towards a formal analysis of the multi-robot task allocation problem using set theory." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1092–104. http://dx.doi.org/10.11591/eei.v10i2.2395.

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Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient

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Farouq, Zitouni, Maamri Ramdane, and Harous Saad. "Towards a formal analysis of the multi-robot task allocation problem using set theory." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1092~1104. https://doi.org/10.11591/eei.v10i2.2395.

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Nowadays, the multi-robot task allocation problem is one of the most challenging problems in multi-robot systems. It concerns the optimal assignment of a set of tasks to several robots while optimizing a given criterion subject to some constraints. This problem is very complex, particularly when handling large groups of robots and tasks. We propose a formal analysis of the task allocation problem in a multi-robot system, based on set theory concepts. We believe that this analysis will help researchers understand the nature of the problem, its time complexity, and consequently develop efficient

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Panchu K., Padmanabhan, M. Rajmohan, R. Sundar, and R. Baskaran. "Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints." Defence Science Journal 68, no. 2 (2018): 175. http://dx.doi.org/10.14429/dsj.68.11187.

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Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 rob

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Choudhury, B. B., and B. B. Biswal. "A PSO based multi-robot task allocation." International Journal of Computational Vision and Robotics 2, no. 1 (2011): 49. http://dx.doi.org/10.1504/ijcvr.2011.039356.

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Dutta, Ayan, Emily Czarnecki, Vladimir Ufimtsev, and Asai Asaithambi. "Correlation clustering-based multi-robot task allocation." ACM SIGAPP Applied Computing Review 19, no. 4 (2020): 5–16. http://dx.doi.org/10.1145/3381307.3381308.

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Street, Charlie, Bruno Lacerda, Manuel Mühlig, and Nick Hawes. "Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty." Journal of Artificial Intelligence Research 79 (January 11, 2024): 137–71. http://dx.doi.org/10.1613/jair.1.15057.

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For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that

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38

Malvankar-Mehta, Monali S., and Siddhartha S. Mehta. "Optimal task allocation in multi-human multi-robot interaction." Optimization Letters 9, no. 8 (2015): 1787–803. http://dx.doi.org/10.1007/s11590-015-0890-7.

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Park, Bumjin, Cheongwoong Kang, and Jaesik Choi. "Cooperative Multi-Robot Task Allocation with Reinforcement Learning." Applied Sciences 12, no. 1 (2021): 272. http://dx.doi.org/10.3390/app12010272.

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This paper deals with the concept of multi-robot task allocation, referring to the assignment of multiple robots to tasks such that an objective function is maximized. The performance of existing meta-heuristic methods worsens as the number of robots or tasks increases. To tackle this problem, a novel Markov decision process formulation for multi-robot task allocation is presented for reinforcement learning. The proposed formulation sequentially allocates robots to tasks to minimize the total time taken to complete them. Additionally, we propose a deep reinforcement learning method to find the

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Huang, Jie, Quanjun Song, and Zhannan Xu. "Multi robot cooperative rescue based on two-stage task allocation algorithm." Journal of Physics: Conference Series 2310, no. 1 (2022): 012091. http://dx.doi.org/10.1088/1742-6596/2310/1/012091.

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Abstract Task allocation is an important issue in the decision-making of multi robot fire rescue in underground garage. In this paper, a multi robot and multi-objective optimization model is constructed. According to the real-time requirements of fire rescue in the underground garage scene, a two-stage multi robot task allocation algorithm based on ant colony and contract net protocol is proposed, which solves the global deficiency of the allocation result based on contract net protocol and the defect that ant colony optimization can not respond to environmental changes in real time. The compa

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Lei, Tingjun, Pradeep Chintam, Chaomin Luo, Lantao Liu, and Gene Eu Jan. "A Convex Optimization Approach to Multi-Robot Task Allocation and Path Planning." Sensors 23, no. 11 (2023): 5103. http://dx.doi.org/10.3390/s23115103.

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In real-world applications, multiple robots need to be dynamically deployed to their appropriate locations as teams while the distance cost between robots and goals is minimized, which is known to be an NP-hard problem. In this paper, a new framework of team-based multi-robot task allocation and path planning is developed for robot exploration missions through a convex optimization-based distance optimal model. A new distance optimal model is proposed to minimize the traveled distance between robots and their goals. The proposed framework fuses task decomposition, allocation, local sub-task al

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42

Mayya, Siddharth, Diego S. D'antonio, David Saldana, and Vijay Kumar. "Resilient Task Allocation in Heterogeneous Multi-Robot Systems." IEEE Robotics and Automation Letters 6, no. 2 (2021): 1327–34. http://dx.doi.org/10.1109/lra.2021.3057559.

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Korsah, G. Ayorkor, Anthony Stentz, and M. Bernardine Dias. "A comprehensive taxonomy for multi-robot task allocation." International Journal of Robotics Research 32, no. 12 (2013): 1495–512. http://dx.doi.org/10.1177/0278364913496484.

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Lee, Dong-Hyun. "Resource-based task allocation for multi-robot systems." Robotics and Autonomous Systems 103 (May 2018): 151–61. http://dx.doi.org/10.1016/j.robot.2018.02.016.

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Dahl, Torbjørn S., Maja Matarić, and Gaurav S. Sukhatme. "Multi-robot task allocation through vacancy chain scheduling." Robotics and Autonomous Systems 57, no. 6-7 (2009): 674–87. http://dx.doi.org/10.1016/j.robot.2008.12.001.

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Keshmiri, Soheil, and Shahram Payandeh. "Multi-robot, dynamic task allocation: a case study." Intelligent Service Robotics 6, no. 3 (2013): 137–54. http://dx.doi.org/10.1007/s11370-013-0130-x.

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Clinch, Katie, Tony A. Wood, and Chris Manzie. "Auction algorithm sensitivity for multi-robot task allocation." Automatica 158 (December 2023): 111239. http://dx.doi.org/10.1016/j.automatica.2023.111239.

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Panchu, K. Padmanabhan, M. Rajmohan, M. R. Sumalatha, and R. Baskaran. "Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm." Journal of Computational and Theoretical Nanoscience 15, no. 2 (2018): 627–36. http://dx.doi.org/10.1166/jctn.2018.7137.

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This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to pe

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CHU, Jing, Yiqiu TIAN, Qi YUE, and Yong HUANG. "Task allocation and path planning for multi-robot systems in intelligent warehousing." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 42, no. 5 (2024): 929–38. https://doi.org/10.1051/jnwpu/20244250929.

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Faced with today's increasingly complex market demands, traditional manual warehouse systems are becoming inadequate, necessitating the urgent intelligent transformation and upgrading of warehouse systems. In this context, this paper aims to design a task allocation and path planning strategy for a multi-robot warehouse system to efficiently accomplish mixed single-robot and multi-robot types of warehouse tasks. The study proposes a warehouse task allocation strategy that incorporates traffic flow impact factors into the auction algorithm, optimizing task allocation by predicting robot density

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Messing, Andrew, Glen Neville, Sonia Chernova, Seth Hutchinson, and Harish Ravichandar. "GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling." International Journal of Robotics Research 41, no. 2 (2021): 232–56. http://dx.doi.org/10.1177/02783649211052066.

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Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence,

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