Relevant bibliographies by topics / Auction-based task allocation

Academic literature on the topic 'Auction-based task allocation'

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Published: 14 March 2023

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Journal articles on the topic "Auction-based task allocation"

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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 (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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Zhang, Jiandong, Yuyang Chen, Qiming Yang, Yi Lu, Guoqing Shi, Shuo Wang, and Jinwen Hu. "Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO." Electronics 11, no. 7 (March 25, 2022): 1028. http://dx.doi.org/10.3390/electronics11071028.

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With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.
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Braquet, Martin, and Efstathios Bakolas. "Greedy Decentralized Auction-based Task Allocation for Multi-Agent Systems." IFAC-PapersOnLine 54, no. 20 (2021): 675–80. http://dx.doi.org/10.1016/j.ifacol.2021.11.249.

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Zhenyu Wu, Minhua Xiao, Bo Jin, and Lin Feng. "Dynamic task allocation based on distance of superior probability auction." Journal of Convergence Information Technology 7, no. 2 (February 29, 2012): 10–17. http://dx.doi.org/10.4156/jcit.vol7.issue2.2.

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Fang, Baofu, Lu Chen, Hao Wang, Shuanglu Dai, and Qiubo Zhong. "Research on Multirobot Pursuit Task Allocation Algorithm Based on Emotional Cooperation Factor." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/864180.

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Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots’ individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.
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Liang, Yajie, Kun Zhou, and Caicong Wu. "Dynamic Task Allocation Method for Heterogenous Multiagent System in Uncertain Scenarios of Agricultural Field Operation." Journal of Physics: Conference Series 2356, no. 1 (October 1, 2022): 012049. http://dx.doi.org/10.1088/1742-6596/2356/1/012049.

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This study focuses on the problem of dynamic task allocation for a heterogeneous multiagent system (MAS) in uncertain scenarios and its application in agricultural field operation. Previous studies lacked robustness or efficiency for uncertain environments especially in agricultural field, such as agent removal, agent inclusion, changes of agent capabilities, and task changes. We present herein a novel concept of the potential field of capability influence (PFCI), and based on which, we can estimate potential overloaded tasks. This provides an opportunity to improve the allocation of the remaining tasks. Then, we propose a heuristic-based clustering auction (HBCA) method by introducing PFCI into the auction mechanism to achieve an effective and efficient task allocation. The whole method consists of the three following phases based on the auction mechanism: 1) auctioneer preprocessing phase that adopts a capability priority strategy to select the preferable agent; 2) bidder preprocessing phase that introduces the PFCI to obtain a list of clustered tasks heuristically; and 3) adaptive auction phase that applies the auction mechanism to achieve an appropriate match between agents and tasks. Numerical simulations with different uncertain field operation scenarios validate the effectiveness of the proposed method. The results show that the proposed HBCA can dramatically reduce the total assignment cost and outperform state-of-the-art methods, especially in the case of obvious variances in agent capabilities.
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He, Jianhua, Siqi Tao, Yang Deng, Libin Chen, and Zhiying Mou. "Research on Multi-Sensor Resource Dynamic Allocation Auction Algorithm." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (April 2019): 330–36. http://dx.doi.org/10.1051/jnwpu/20193720330.

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This paper designs a multi-sensor resource dynamic allocation method based on auction algorithm. Tasks are prioritized according to the needs of the engineering field. Task priority is used as the basis for multi-sensor resource allocation order, taking into account the target's threat value and information needs. The sensor and task pairing function is established and used to measure the sensor resource dynamic allocation, we also use Analytic Hierarchy Process to determine the weight of each performance parameter in the pairing function (such as detection probability, intercept probability, positioning accuracy, tracking accuracy, recognition probability, etc.). The auction algorithm is improved by adding resource dynamic allocation constraints, which not only ensures the continuous execution of the target task, but also improves the dynamic allocation efficiency of multi-sensor resources. The simulation results show that the allocation method in this paper is scientific and reasonable.
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Fazal, Nayyer, Muhammad Tahir Khan, Shahzad Anwar, Javaid Iqbal, and Shahbaz Khan. "Task allocation in multi-robot system using resource sharing with dynamic threshold approach." PLOS ONE 17, no. 5 (May 4, 2022): e0267982. http://dx.doi.org/10.1371/journal.pone.0267982.

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Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.
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LIANG, Zhiwei, Jie SHEN, Xiang YANG, Juan LIU, and Songhao ZHU. "Task Allocation Algorithm Based on Auction in RoboCup Rescue Robot Simulation." Robot 35, no. 4 (2013): 410. http://dx.doi.org/10.3724/sp.j.1218.2013.00410.

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Chen, Chao, Weidong Bao, Tong Men, Xiaomin Zhu, Ji Wang, and Rui Wang. "NECTAR-An Agent-Based Dynamic Task Allocation Algorithm in the UAV Swarm." Complexity 2020 (September 16, 2020): 1–14. http://dx.doi.org/10.1155/2020/6747985.

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The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-based allocating mechanism based on the auction process, including the design for three kinds of agents and the cooperation mechanism among different agents. Moreover, we proposed a new algorithm to calculate the bidding values of UAVs, by which the messages passed between UAVs can be reduced. On the basis of the assigning mechanism, we put up with a novel agent-based real-time task allocation algorithm named NECTAR for dynamic tasks in the UAV swarm. Furthermore, we conduct extensive experiments to evaluate the performance of our NECTAR, and the results indicate that NECTAR is able to solve the real-time task allocation for dynamic tasks and achieve high performance of the UAV swarm.
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Dissertations / Theses on the topic "Auction-based task allocation"

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Koung, Daravuth. "Cooperative navigation of a fleet of mobile robots." Electronic Thesis or Diss., Ecole centrale de Nantes, 2022. http://www.theses.fr/2022ECDN0044.

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L’intérêt pour l’intégration des systèmes multi-robots (MRS) dans les applications du monde réel augmente de plus en plus, notamment pour l’exécution de tâches complexes. Pour les tâches de transport de charges, différentes stratégies de manutention de charges ont été proposées telles que : la poussée seule, la mise en cage et la préhension. Dans cette thèse, nous souhaitons utiliser une stratégie de manipulation simple : placer l’objet à transporter au sommet d’un groupe de robots mobiles. Ainsi, cela nécessite un contrôle de formation rigide. Nous proposons deux algorithmes de formation. L’algorithme de consensus est l’un d’entre eux. Nous adaptons un contrôleur de flocking dynamique pour qu’il soit utilisé dans le système à un seul intégrateur, et nous proposons un système d’évitement d’obstacles qui peut empêcher le fractionnement tout en évitant les obstacles. Le deuxième contrôle de formation est basé sur l’optimisation quadratique hiérarchique (HQP). Le problème est décomposé en plusieurs objectifs de tâches : formation, navigation,évitement d’obstacles et limites de vitesse. Ces tâches sont représentées par des contraintes d’égalité et d’inégalité avec différentsniveaux de priorité, qui sont résolues séquentiellement par le HQP. Enfin, une étude sur les algorithmes d’allocation des tâches(Contract Net Protocol et Tabu Search) est menée afin de déterminer une solution appropriée pour l’allocation des tâches dans l’environnementindustriel
The interest in integrating multirobot systems (MRS) into real-world applications is increasing more and more, especially for performing complex tasks. For loadcarrying tasks, various load-handling strategies have been proposed such as: pushingonly, caging, and grasping. In this thesis, we aim to use a simple handling strategy: placing the carrying object on top of a group of wheeled mobile robots. Thus, it requires a rigid formation control. A consensus algorithm is one of the two formation controllers we apply to the system. We adapt a dynamic flocking controller to be used in the singleintegrator system, and we propose an obstacle avoidance that can prevent splitting while evading the obstacles. The second formation control is based on hierarchical quadratic programming (HQP). The problem is decomposed into multiple task objectives: formation, navigation, obstacle avoidance, velocity limits. These tasks are represented by equality and inequality constraints with different levels of priority, which are solved sequentially by the HQP. Lastly, a study on task allocation algorithms (Contract Net Protocol and Tabu Search) is carried out in order to determine an appropriate solution for allocating tasks in the industrial environment
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Book chapters on the topic "Auction-based task allocation"

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Pereira, Eliseu, João Reis, Gil Gonçalves, Luís Paulo Reis, and Ana Paula Rocha. "Dutch Auction Based Approach for Task/Resource Allocation." In Lecture Notes in Mechanical Engineering, 322–33. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79168-1_30.

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Tao, Xue-li, and Yan-bin Zheng. "Multi-agent Task Allocation Method Based on Auction." In Lecture Notes in Electrical Engineering, 217–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14350-2_27.

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Shi, Jieke, Zhou Yang, and Junwu Zhu. "An Auction-Based Task Allocation Algorithm in Heterogeneous Multi-Robot System." In 2nd EAI International Conference on Robotic Sensor Networks, 149–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17763-8_14.

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Schneider, Eric, Elizabeth I. Sklar, Simon Parsons, and A. Tuna Özgelen. "Auction-Based Task Allocation for Multi-robot Teams in Dynamic Environments." In Towards Autonomous Robotic Systems, 246–57. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22416-9_29.

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Xiao, Junlei, Peng Li, and Lei Nie. "A Reliable Multi-task Allocation Based on Reverse Auction for Mobile Crowdsensing." In Wireless Algorithms, Systems, and Applications, 529–41. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59016-1_44.

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Wei, Changyun, Koen V. Hindriks, and Catholijn M. Jonker. "Auction-Based Dynamic Task Allocation for Foraging with a Cooperative Robot Team." In Multi-Agent Systems, 159–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17130-2_11.

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Bao, Kanghua, Shuang Yi, Hongjiang Zhang, and Pengsheng He. "Multi-unmanned Aerial Vehicle Cooperative Task Allocation Algorithm Based on Improved Distributed Cooperative Auction." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022), 1535–43. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_141.

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Quinton, Félix, Christophe Grand, and Charles Lesire. "Improving the Connectivity of Multi-hop Communication Networks Through Auction-Based Multi-robot Task Allocation." In Lecture Notes in Computer Science, 345–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18192-4_28.

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Nazif, Ali Nasri, Ehsan Iranmanesh, and Ali Mohades. "Multiple Robots Tasks Allocation: An Auction-Based Approach Using Dynamic-Domain RRT." In Communications in Computer and Information Science, 795–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_105.

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Wang, Bo, and Mingchu Li. "Resource Allocation Scheduling Algorithm Based on Incomplete Information Dynamic Game for Edge Computing." In Research Anthology on Edge Computing Protocols, Applications, and Integration, 414–39. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5700-9.ch021.

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With the advent of the 5G era, the demands for features such as low latency and high concurrency are becoming increasingly significant. These sophisticated new network applications and services require huge gaps in network transmission bandwidth, network transmission latency, and user experience, making cloud computing face many technical challenges in terms of applicability. In response to cloud computing's shortcomings, edge computing has come into its own. However, many factors affect task offloading and resource allocation in the edge computing environment, such as the task offload latency, energy consumption, smart device mobility, end-user power, and other issues. This paper proposes a dynamic multi-winner game model based on incomplete information to solve multi-end users' task offloading and edge resource allocation. First, based on the history of end-users storage in edge data centers, a hidden Markov model can predict other end-users' bid prices at time t. Based on these predicted auction prices, the model determines their bids. A dynamic multi-winner game model is used to solve the offload strategy that minimizes latency, energy consumption, cost, and to maximizes end-user satisfaction at the edge data center. Finally, the authors designed a resource allocation algorithm based on different priorities and task types to implement resource allocation in edge data centers. To ensure the prediction model's accuracy, the authors also use the expectation-maximization algorithm to learn the model parameters. Comparative experimental results show that the proposed model can better results in time delay, energy consumption, and cost.
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Conference papers on the topic "Auction-based task allocation"

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Gurel, Ugur, Nihat Adar, and Osman Parlaktuna. "Priority-based task allocation in auction-based applications." In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2013. http://dx.doi.org/10.1109/inista.2013.6577654.

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Harman, Helen, and Elizabeth Sklar. "Auction-based Task Allocation Mechanisms for Managing Fruit Harvesting Tasks." In UKRAS21 Conference: Robotics at home. EPSRC UK-RAS Network, 2021. http://dx.doi.org/10.31256/dg2zp9q.

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Hoeing, Matthew, Prithviraj Dasgupta, Plamen Petrov, and Stephen O'Hara. "Auction-based multi-robot task allocation in COMSTAR." In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329462.

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Wen, Xiao, and Zhen-Gang Zhao. "Multi-Robot Task Allocation Based on Combinatorial Auction." In 2021 9th International Conference on Control, Mechatronics and Automation (ICCMA). IEEE, 2021. http://dx.doi.org/10.1109/iccma54375.2021.9646189.

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Zhang, Ziying, Jie Wang, Dong Xu, and Yulong Meng. "Task Allocation of Multi-AUVs Based on Innovative Auction Algorithm." In 2017 10th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2017. http://dx.doi.org/10.1109/iscid.2017.231.

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Wu, Shiguang, Xiaojie Liu, Xingwei Wang, Xiaolin Zhou, and Mingyang Sun. "Multi-robot Dynamic Task Allocation Based on Improved Auction Algorithm." In 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE). IEEE, 2021. http://dx.doi.org/10.1109/cacre52464.2021.9501305.

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Mingqiu, Ren, Liu Junkai, and Shang Ben. "Adaptive Task Scheduling and Resources Allocation Based on Auction Algorithm." In 2021 13th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2021. http://dx.doi.org/10.1109/ihmsc52134.2021.00052.

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Butler, Zack. "Auction-based task allocation for teams of self-reconfigurable robots." In 2012 IEEE International Symposium on Intelligent Control (ISIC). IEEE, 2012. http://dx.doi.org/10.1109/isic.2012.6398262.

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Edalat, Neda, Wendong Xiao, Nirmalya Roy, Sajal K. Das, and Mehul Motani. "Combinatorial Auction-Based Task Allocation in Multi-application Wireless Sensor Networks." In 2011 IEEE/IFIP 9th International Conference on Embedded and Ubiquitous Computing (EUC). IEEE, 2011. http://dx.doi.org/10.1109/euc.2011.22.

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Zhao, Han, and Xiaolin Li. "Efficient Grid Task-Bundle Allocation Using Bargaining Based Self-Adaptive Auction." In 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid. IEEE, 2009. http://dx.doi.org/10.1109/ccgrid.2009.86.

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