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Статті в журналах з теми "Allocation de tâches multi-Robots (MRTA)"
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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 (March 20, 2023): 3284. http://dx.doi.org/10.3390/s23063284.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
<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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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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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.
Повний текст джерелаАнотація:
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.
Дисертації з теми "Allocation de tâches multi-Robots (MRTA)"
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Chakraa, Hamza. "Οptimisatiοn techniques fοr mοnitοring a high-risk industrial area by a team οf autοnοmοus mοbile rοbοts". Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH29.
Повний текст джерелаАнотація:
Cette thèse explore le développement et la mise en œuvre d’algorithmes d’optimisation pour la surveillance de zones industrielles à l’aide d’une équipe de robots mobiles autonomes. Le travail de recherche se concentre sur l’allocation de tâches multi-robots (MRTA), où un plan de mission quasi-optimal doit être généré. Un nouveau modèle prenant en compte des robots et des tâches hétérogènes est proposé, utilisant des algorithmes génétiques (GA) et une méthode de recherche locale 2-Opt pour résoudre le problème. La thèse intègre également des stratégies d’évitement des collisions, qui deviennent nécessaires lorsqu’il y a beaucoup de robots et de tâches. Une solution locale de bas niveau gère de nombreuses situations de conflit pendant la mission, ce qui peut entraîner des retards. Par conséquent, une solution pour ce cas a été proposée en utilisant le clustering. En outre, nous évaluons les solutions proposées à l’aide d’expériences réelles qui incluent un algorithme basé sur la navigation pour résoudre les problèmes de collision. Les résultats démontrent la valeur de ces algorithmes dans l’optimisation de la répartition des tâches et de la planification des chemins pour les robots mobiles autonomes dans les environnements industriels, ouvrant la voie à une planification de mission plus efficace et à une sécurité accrue dans les environnements industrielsThis thesis explores the development and implementation of optimisation algorithms for monitoring industrial areas using a team of autonomous mobile robots. The research focuses on Multi-Robot Task Allocation (MRTA), where a near-optimal mission plan must be generated. A novel model considering heterogeneous robots and tasks is proposed, using Genetic Algorithms (GA) and 2-Opt local search methods to solve the problem. The thesis also integrates collision avoidance strategies, which become necessary when there are many robots and tasks. A low-level local solution handles many conflict situations during the mission, which can cause delays. Therefore, a solution for this case was proposed using clustering. Furthermore, we evaluate the proposed solutions through real-world experiments including a navigation-based algorithm that addresses collision issues. The results demonstrate the value of these algorithms in optimising task allocation and path planning for autonomous mobile robots in industrial settings, paving the way for more efficient mission planning and enhanced safety in industrial environments
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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.
Повний текст джерелаАнотація:
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’environnementindustrielThe 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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Bautin, Antoine. "Stratégie d'exploration multirobot fondée sur le calcul de champs de potentiels." Thesis, Université de Lorraine, 2013. http://www.theses.fr/2013LORR0261/document.
Повний текст джерелаАнотація:
Cette thèse s'inscrit dans le cadre du projet Cart-O-Matic mis en place pour participer au défi CAROTTE (CArtographie par ROboT d'un TErritoire) organisé par l'ANR et la DGA. Le but de ce défi est de construire une carte en deux et trois dimensions et de localiser des objets dans un environnement inconnu statique de type appartement. Dans ce contexte, l'utilisation de plusieurs robots est avantageuse car elle permet d'augmenter l'efficacité en temps de la couverture. Cependant, comme nous le montrons, le gain est conditionné par le niveau de coopération entre les robots. Nous proposons une stratégie de coopération pour une cartographie multirobot efficace. Une difficulté est la construction d'une carte commune, nécessaire, afin que chaque robot puisse connaître les zones de l'environnement encore inexplorées. Pour obtenir une bonne coopération avec un algorithme simple nous proposons une technique de déploiement fondée sur le choix d'une cible par chaque robot. L'algorithme proposé cherche à distribuer les robots vers différentes directions. Il est fondé sur le calcul partiel de champs de potentiels permettant à chaque robot de calculer efficacement son prochain objectif. En complément de ces contributions théoriques, nous décrivons le système robotique complet mis en oeuvre au sein de l'équipe Cart-O-Matic ayant permis de remporter la dernière édition du défi CAROTTEThis thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge
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Bautin, Antoine. "Stratégie d'exploration multirobot fondée sur le calcul de champs de potentiels." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0261.
Повний текст джерелаАнотація:
Cette thèse s'inscrit dans le cadre du projet Cart-O-Matic mis en place pour participer au défi CAROTTE (CArtographie par ROboT d'un TErritoire) organisé par l'ANR et la DGA. Le but de ce défi est de construire une carte en deux et trois dimensions et de localiser des objets dans un environnement inconnu statique de type appartement. Dans ce contexte, l'utilisation de plusieurs robots est avantageuse car elle permet d'augmenter l'efficacité en temps de la couverture. Cependant, comme nous le montrons, le gain est conditionné par le niveau de coopération entre les robots. Nous proposons une stratégie de coopération pour une cartographie multirobot efficace. Une difficulté est la construction d'une carte commune, nécessaire, afin que chaque robot puisse connaître les zones de l'environnement encore inexplorées. Pour obtenir une bonne coopération avec un algorithme simple nous proposons une technique de déploiement fondée sur le choix d'une cible par chaque robot. L'algorithme proposé cherche à distribuer les robots vers différentes directions. Il est fondé sur le calcul partiel de champs de potentiels permettant à chaque robot de calculer efficacement son prochain objectif. En complément de ces contributions théoriques, nous décrivons le système robotique complet mis en oeuvre au sein de l'équipe Cart-O-Matic ayant permis de remporter la dernière édition du défi CAROTTEThis thesis is part of Cart-O-Matic project set up to participate in the challenge CARROTE (mapping of a territory) organized by the ANR and the DGA. The purpose of this challenge is to build 2D and 3D maps of a static unknown 'apartment-like' environment. In this context, the use of several robots is advantageous because it increases the time efficiency to discover fully the environment. However, as we show, the gain is determined by the level of cooperation between robots. We propose a cooperation strategy for efficient multirobot mapping. A difficulty is the construction of a common map, necessary so that each robot can know the areas of the environment which remain unexplored.For a good cooperation with a simple algorithm we propose a deployment technique based on the choice of a target by each robot. The proposed algorithm tries to distribute the robots in different directions. It is based on calculation of the partial potential fields allowing each robot to compute efficiently its next target. In addition to these theoretical contributions, we describe the complete robotic system implemented in the Cart-O-Matic team that helped win the last edition of the CARROTE challenge
Частини книг з теми "Allocation de tâches multi-Robots (MRTA)"
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Koubaa, Anis, Sahar Trigui, and Imen Chaari. "Indoor Surveillance Application using Wireless Robots and Sensor Networks." In Mobile Ad Hoc Robots and Wireless Robotic Systems, 19–57. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2658-4.ch002.
Повний текст джерелаАнотація:
Mobile robots and Wireless Sensor Networks (WSNs) are enabling technologies of ubiquitous and pervasive applications. Surveillance is one typical example of such applications for which the literature proposes several solutions using mobile robots and/or WSNs. However, robotics and WSNs have mostly been considered as separate research fields, and little work has investigated the marriage of these two technologies. In this chapter, the authors propose an indoor surveillance application, SURV-TRACK, which controls a team of multiple cooperative robots supported by a WSN infrastructure. They propose a system model for SURV-TRACK to demonstrate how robots and WSNs can complement each other to efficiently accomplish the surveillance task in a distributed manner. Furthermore, the authors investigate two typical underlying problems: (1) Multi-Robot Task Allocation (MRTA) for target tracking and capturing and (2) robot path planning. The novelty of the solutions lies in incorporating a WSN in the problems’ models. The authors believe that this work advances the literature by demonstrating a concrete ubiquitous application that couples robotic and WSNs and proposes new solutions for path planning and MRTA problems.
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Pal, Aritra, Anandsingh Chauhan, Mayank Baranwal, and Ankush Ojha. "Optimizing Multi-Robot Task Allocation in Dynamic Environments via Heuristic-Guided Reinforcement Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240705.
Повний текст джерелаАнотація:
In modern warehousing environments, efficient task allocation among multiple robots is crucial for optimizing productivity and meeting the ever-increasing demands of online order fulfillment. In this paper, we address the challenging problem of real-time multi-robot task allocation (MRTA) in a warehouse setting, where tasks appear dynamically with corresponding start and end locations. The objective is to minimize both the total travel distance of robots and the delay in task execution while considering practical charging/discharging constraints and collision-free navigation. To tackle this combinatorially hard problem, we propose a heuristic guided reinforcement learning (RL) agent, HeuRAL-MATE, which learns to prioritize prompt task execution while optimizing the assignment of tasks to robots. Our proposed approach outperforms standard practices like First-In-First-Out (FIFO), as well as a brute-force optimal approach in terms of efficiency and performance. The results on multiple synthetic datasets exhibit an average cost reduction of approximately 8.58% and 10.74% in total expenses when compared with brute-force optimal approach and FIFO, respectively.
Тези доповідей конференцій з теми "Allocation de tâches multi-Robots (MRTA)"
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Agrawal, Aakriti, Senthil Hariharan, Amrit Singh Bedi, and Dinesh Manocha. "DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981353.
Повний текст джерела
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Chuang, Ching-Wei, and Harry H. Cheng. "A Novel Approach With Bayesian Networks to Multi-Robot Task Allocation in Dynamic Environments." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-66902.
Повний текст джерелаАнотація:
Abstract In the modern world, building an autonomous multi-robot system is essential to coordinate and control robots to help humans because using several low-cost robots becomes more robust and efficient than using one expensive, powerful robot to execute tasks to achieve the overall goal of a mission. One research area, multi-robot task allocation (MRTA), becomes substantial in a multi-robot system. Assigning suitable tasks to suitable robots is crucial in coordination, which may directly influence the result of a mission. In the past few decades, although numerous researchers have addressed various algorithms or approaches to solve MRTA problems in different multi-robot systems, it is still difficult to overcome certain challenges, such as dynamic environments, changeable task information, miscellaneous robot abilities, the dynamic condition of a robot, or uncertainties from sensors or actuators. In this paper, we propose a novel approach to handle MRTA problems with Bayesian Networks (BNs) under these challenging circumstances. Our experiments exhibit that the proposed approach may effectively solve real problems in a search-and-rescue mission in centralized, decentralized, and distributed multi-robot systems with real, low-cost robots in dynamic environments. In the future, we will demonstrate that our approach is trainable and can be utilized in a large-scale, complicated environment. Researchers might be able to apply our approach to other applications to explore its extensibility.
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Yan, Fuhan, and Kai Di. "Multi-robot Task Allocation in the Environment with Functional Tasks." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/653.
Повний текст джерелаАнотація:
Multi-robot task allocation (MRTA) problem has long been a key issue in multi-robot systems. Previous studies usually assumed that the robots must complete all tasks with minimum time cost. However, in many real situations, some tasks can be selectively performed by robots and will not limit the achievement of the goal. Instead, completing these tasks will cause some functional effects, such as decreasing the time cost of completing other tasks. This kind of task can be called “functional task”. This paper studies the multi-robot task allocation in the environment with functional tasks. In the problem, neither allocating all functional tasks nor allocating no functional task is always optimal. Previous algorithms usually allocate all tasks and cannot suitably select the functional tasks. Because of the interaction and sequential influence, the total effects of the functional tasks are too complex to exactly calculate. We fully analyze this problem and then design a heuristic algorithm. The heuristic algorithm scores the functional tasks referring to linear threshold model (used to analyze the sequential influence of a functional task). The simulated experiments demonstrate that the heuristic algorithm can outperform the benchmark algorithms.