Статті в журналах: "Multi-Robot task allocation (MRTA)"

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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2

Gul, Omer Melih. "Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks." Sensors 23, no. 6 (March 20, 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 robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

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3

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 (March 31, 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 allocations. The most prominent contribution of this work is Rostam's flexible design which enables it to handle a range of task allocation scenarios seamlessly. Rostam's performance is evaluated against an auction-based scheme; the results demonstrate its effective use of multi-tasking robots. The paper also demonstrates Rostam's flexibility towards a number of MRTA scenarios through a case study.

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4

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 allocation problem of large state space and high complexity utilizing deep learning. First, a multi-agent framework based on cooperative structure is proposed according to the characteristics of RMFS. Then, a multi agent task allocation model is constructed based on Markov Decision Process. In order to avoid inconsistent information among agents and improve the convergence speed of traditional Deep Q Network (DQN), an improved DQN algorithm based on a shared utilitarian selection mechanism and priority empirical sample sampling is proposed to solve the task allocation model. Simulation results show that the task allocation algorithm based on deep reinforcement learning is more efficient than that based on a market mechanism, and the convergence speed of the improved DQN algorithm is much faster than that of the original DQN algorithm.</p></abstract>

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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 (November 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 maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

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6

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 (July 28, 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 allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.

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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 (March 24, 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 centralized learning and decentralized execution paradigm was used for training the policy. The policy was evaluated quantitatively and visually. The simulation results showed the effectiveness of the proposed method deployed on multiple Turtlebot3 robots.

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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 (August 14, 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 harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.

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9

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 (December 12, 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 evolutionary direction during the proceeding of multi-objective evolutionary algorithms, aiming to promote convergence of the Pareto front. Numerical experiments on the benchmark show the LKH guidance mechanism is effective for two famous multi-objective evolutionary algorithms, namely multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm (NSGA), of which LKH-guided NSGA exhibits the best performance on three predefined indicators, namely C-metric, HV, and Spacing, respectively. The generalization experiment on a multiple depots MRTA problem with constraints further demonstrates the effectiveness of the proposed approach for practical decision making.

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10

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 (May 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 to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.

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11

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 (June 1, 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 the cost functions of all agents involved. Our evaluation meticulously examines the MRTA efficiency for each micro-robot, considering dependencies on factors such as radio connectivity, available energy, and the absolute and relative availability of agents. The central focus is on validating the positive trend associated with an increasing number of agents constituting the cluster. Our methodology introduces a trio of micro-robots, unveiling a flexible strategy aimed at detecting individuals at risk in demanding environments. Each micro-robot within the cluster is equipped with logic that ensures compatibility and cooperation, enabling them to effectively execute assigned missions. The implementation of MRTA-based collaboration algorithms serves as an adaptive strategy, optimizing agents' mobility based on specific criteria related to the characteristics of the target site.<p class="JAMRISAbstract"> </p>

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12

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 is implemented between the two approaches and the results show that the optimization-based approach outperforms the market-based approach in terms of optimal allocation and computational time.

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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 (July 31, 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 and effectiveness of Multi-Robot Task Allocation (MRTA) compared to traditional methods that lack optimization. By incorporating a cost function with various weighted factors, the task allocation process becomes both comprehensive and adaptable to specific mission requirements. This ensures that the robots can allocate tasks efficiently, even as conditions change. This study underscores the potential of adaptive genetic algorithms to advance the capabilities of mobile multi-robot systems, particularly in applications that require high levels of collaboration and precision. Our approach not only improves task allocation efficiency but also enhances the overall coordination and performance of the robotic system. The adaptability and robustness of the GA make it a promising solution for real-world applications, including search-and-rescue missions, environmental monitoring, and industrial automation. So, the potential of adaptive genetic algorithm presents a significant advancement in optimizing mobile multi-robot collaboration. Also, its ability to dynamically adjust to changing conditions and improve task allocation processes highlights its potential for a wide range of applications, marking a notable step forward in the field of collaborative robotics.

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14

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 (September 14, 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 (September 26, 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 a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO. Design/methodology/approach This approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid. Findings Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time. Practical implications The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks. Originality/value In this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

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16

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 (September 30, 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 to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach.

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17

Baroudi, Uthman, Mohammad Alshaboti, Anis Koubaa, and Sahar Trigui. "Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation." Applied Sciences 10, no. 9 (May 8, 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 approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.

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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 correlation is built according to the characteristics of the picking process. Then, a multirobot task allocation model minimizing the overall picking time is established considering both the picking time balance of picking stations and the load balance of robots. Finally, a four-stage balanced heuristic auction algorithm is designed to solve the task allocation model and the tasks with execution sequence for each robot are obtained. By comparing with the traditional task time cost model and the algorithm without considering the balance among picking stations, it is found that the proposed model and algorithm can significantly shorten the overall picking time.

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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 (December 15, 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 limitations, this paper proposes a task allocation method based on the Monte Carlo Tree Search (MCTS) algorithm, which leverages the anytime property of MCTS to achieve a balance between fast response and continuous optimization. Firstly, the centralized adaptive MCTS algorithm generates preliminary solutions and monitors the state of the robots in minimal time. It utilizes dynamic Upper Confidence Bounds for Trees (UCT) values to accommodate varying task dimensions, outperforming the heuristic Multi-Robot Goal Assignment (MRGA) method in both planning time and overall task completion time. Furthermore, the parallelized distributed MCTS algorithm reduces algorithmic complexity and enhances computational efficiency through importance sampling and parallel processing. Experimental results demonstrate that the proposed method significantly reduces computation time while maintaining task allocation performance, decreasing the variance of planning results and improving algorithmic stability. Our approach enables more flexible and efficient task allocation in dynamically evolving and complex environments, providing robust support for the deployment of multi-robot systems.

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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 TQS protocol was inspired by multiprotocol label switching (MPLS), achieving a quality of service (QoS) where swapping and labeling operations involving the data packet header were applied. The OMNET++ discrete event simulator was used to analyze the percentage of losses, average access delay, and throughput of the transmitted data in diﬀerent classes of service (CoS), in a line of transmission between underwater and land environments. The results show that inferior data transmission performance has the lowest priority with low bitrates and extremely high data packet loss rates when the network traﬃc was busy. On the other hand, simulation results for the highest CoS data forwarding show that its performance was not aﬀected by diﬀerent data transmission rates characterizing diﬀerent mediums and environments.

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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 called COMRADES to address different aspects of the autonomous landmine detection problem including distributed area coverage to detect and locate landmines, information aggregation to fuse the sensor information obtained by different robots, and multirobot task allocation (MRTA) to enable different robots to determine a suitable sequence to visit locations of potential landmines while reducing the time required and battery expended. We have used commercially available all-terrain robots called Coroware Explorer that are customized with a metal detector to detect metallic objects including landmines, as well as indoor Corobot robots, both in simulation and in physical experiments, to test the different techniques in COMRADES.

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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 (October 31, 2017): 864–75. http://dx.doi.org/10.18038/aubtda.340101.

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23

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

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24

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 (September 11, 2023): 3842. http://dx.doi.org/10.3390/electronics12183842.

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Анотація:

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 unbalanced cost consumed by each robot after clustering assignment, which leads to low efficiency of system completion, a two-stage load-balancing strategy is presented. The first stage of this strategy makes a large adjustment to the unbalanced task set, and the second stage achieves a small fine-tuning to the unbalanced task set. The experimental results show that the standard deviation of the cost ratio of each set decreases when four robots perform the task between 100 and 550 work points using the load-balancing strategy. The reduction in standard deviation is between 3.53% and 83.44%. The maximum cost of individual robots decreases between 0.18% and 14.27%. The proposed method can effectively solve the uneven execution cost of each robot in the task assignment process and improve the efficiency of the system in completing tasks.

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25

CHOUDHURY, B. B., and B. B. BISWAL. "ALTERNATIVE METHODS FOR MULTI-ROBOT TASK ALLOCATION." Journal of Advanced Manufacturing Systems 08, no. 02 (December 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 space. The present work adopts a two-phase methodology to allocate tasks optimally amongst the candidate robots. The allocation cost of the robots is determined during the first phase and alternate algorithms are used in the second phase for optimizing the allocation. The work considers systems of practical sizes and the results obtained through this are helpful in recommending appropriate techniques to the users of MRS for increasing producibility and robot utilization. Three different approaches using Linear programming, Hungarian Algorithm and Knapsack Algorithm are presented and their results are analyzed for the suitability of the methods for an allocation problem. Simulation results are presented and compared for the benefit of the users.

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26

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 (September 21, 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 establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems. Findings This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies. Originality/value This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

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27

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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28

Dutta, Ayan, Emily Czarnecki, Vladimir Ufimtsev, and Asai Asaithambi. "Correlation clustering-based multi-robot task allocation." ACM SIGAPP Applied Computing Review 19, no. 4 (January 28, 2020): 5–16. http://dx.doi.org/10.1145/3381307.3381308.

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29

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 (March 13, 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 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level.

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30

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 (April 1, 2021): 1092–104. http://dx.doi.org/10.11591/eei.v10i2.2395.

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Анотація:

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 solutions. Also, we used that formal analysis to formulate two well-known taxonomies of multi-robot task allocation problems. Finally, a generic solving scheme of multi-robot task allocation problems is proposed and illustrated on assigning papers to reviewers within a journal.

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31

Malvankar-Mehta, Monali S., and Siddhartha S. Mehta. "Optimal task allocation in multi-human multi-robot interaction." Optimization Letters 9, no. 8 (April 21, 2015): 1787–803. http://dx.doi.org/10.1007/s11590-015-0890-7.

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32

Park, Bumjin, Cheongwoong Kang, and Jaesik Choi. "Cooperative Multi-Robot Task Allocation with Reinforcement Learning." Applied Sciences 12, no. 1 (December 28, 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 best allocation schedule for each problem. Our method adopts the cross-attention mechanism to compute the preference of robots to tasks. The experimental results show that the proposed method finds better solutions than meta-heuristic methods, especially when solving large-scale allocation problems.

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33

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 robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models.

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34

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 (April 2021): 1327–34. http://dx.doi.org/10.1109/lra.2021.3057559.

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35

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 (October 2013): 1495–512. http://dx.doi.org/10.1177/0278364913496484.

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36

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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37

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 (June 2009): 674–87. http://dx.doi.org/10.1016/j.robot.2008.12.001.

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38

Keshmiri, Soheil, and Shahram Payandeh. "Multi-robot, dynamic task allocation: a case study." Intelligent Service Robotics 6, no. 3 (March 31, 2013): 137–54. http://dx.doi.org/10.1007/s11370-013-0130-x.

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39

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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40

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 (October 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 comparative test results in dynamic environment show that the two-stage task allocation algorithm is similar to ant colony optimization in global cost, and its real-time performance is better than ant colony optimization.

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41

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 (May 26, 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 allocation, and path planning. To begin, multiple robots are firstly divided and clustered into a variety of teams considering interrelation and dependencies of robots, and task decomposition. Secondly, the teams with various arbitrary shape enclosing intercorrelative robots are approximated and relaxed into circles, which are mathematically formulated to convex optimization problems to minimize the distance between teams, as well as between a robot and their goals. Once the robot teams are deployed into their appropriate locations, the robot locations are further refined by a graph-based Delaunay triangulation method. Thirdly, in the team, a self-organizing map-based neural network (SOMNN) paradigm is developed to complete the dynamical sub-task allocation and path planning, in which the robots are dynamically assigned to their nearby goals locally. Simulation and comparison studies demonstrate the proposed hybrid multi-robot task allocation and path planning framework is effective and efficient.

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42

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 (February 1, 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 perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

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43

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 (October 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 in various areas of the environment. For multi-robot formation tasks, a three-robot formation model based on the virtual structure method is designed. Additionally, a two-layer path planning strategy is proposed: the outer layer conducts global path planning based on the Floyd algorithm, while the inner layer resolves various collision issues through traffic rule constraints, achieving local optimal path planning. Simulation experiments conducted on the MATLAB platform show that the multi-robot system can flexibly handle mixed types of warehouse tasks, effectively reducing collision risks between robots and stagnation in dense areas, thereby improving the safety and efficiency of the multi-robot system. This study provides a reference for future research and practical applications of multi-robot systems.

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44

Cechinel, Alan Kunz, Edson Roberto De Pieri, Anderson Luiz Fernandes Perez, and Patricia Della Méa Plentz. "Multi-robot Task Allocation Using Island Model Genetic Algorithm." IFAC-PapersOnLine 54, no. 1 (2021): 558–63. http://dx.doi.org/10.1016/j.ifacol.2021.08.063.

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45

Lerman, Kristina, Chris Jones, Aram Galstyan, and Maja J. Matarić. "Analysis of Dynamic Task Allocation in Multi-Robot Systems." International Journal of Robotics Research 25, no. 3 (March 2006): 225–41. http://dx.doi.org/10.1177/0278364906063426.

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46

Baghaei, Khashayar R., and Arvin Agah. "Task Allocation and Communication Methodologies for Multi-Robot Systems." Intelligent Automation & Soft Computing 9, no. 4 (January 2003): 217–26. http://dx.doi.org/10.1080/10798587.2000.10642855.

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47

Hussein, Ahmed, Mohamed Adel, Mohamed Bakr, Omar M. Shehata, and Alaa Khamis. "Multi-robot Task Allocation for Search and Rescue Missions." Journal of Physics: Conference Series 570, no. 5 (December 16, 2014): 052006. http://dx.doi.org/10.1088/1742-6596/570/5/052006.

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48

Dai, Wei, Huimin Lu, Junhao Xiao, Zhiwen Zeng, and Zhiqiang Zheng. "Multi-Robot Dynamic Task Allocation for Exploration and Destruction." Journal of Intelligent & Robotic Systems 98, no. 2 (October 25, 2019): 455–79. http://dx.doi.org/10.1007/s10846-019-01081-3.

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49

Kloetzer, Marius, Adrian Burlacu, and Doru Panescu. "On a Class of Multi-Robot Task Allocation Problems." IFAC Proceedings Volumes 45, no. 6 (May 2012): 841–46. http://dx.doi.org/10.3182/20120523-3-ro-2023.00327.

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50

Hojda, Maciej. "Task allocation in robot systems with multi-modal capabilities." IFAC-PapersOnLine 48, no. 3 (2015): 2109–14. http://dx.doi.org/10.1016/j.ifacol.2015.06.400.

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