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Chakraa, Hamza. "Οptimisatiοn techniques fοr mοnitοring a high-risk industrial area by a team οf autοnοmοus mοbile rοbοts." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMLH29.
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Cette thèse explore le développement et la mise en œuvre d’algorithmes d’optimisation pour la surveillance de zones industrielles à l’aide d’une équipe de robots mobiles autonomes. Le travail de recherche se concentre sur l’allocation de tâches multi-robots (MRTA), où un plan de mission quasi-optimal doit être généré. Un nouveau modèle prenant en compte des robots et des tâches hétérogènes est proposé, utilisant des algorithmes génétiques (GA) et une méthode de recherche locale 2-Opt pour résoudre le problème. La thèse intègre également des stratégies d’évitement des collisions, qui deviennent 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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Sarker, Md Omar Faruque. "Self-regulated multi-robot task allocation." Thesis, University of South Wales, 2010. https://pure.southwales.ac.uk/en/studentthesis/selfregulated-multirobot-task-allocation(4b92f28f-c712-4e75-955f-97b4e5bf12dd).html.
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To deploy a large group of autonomous robots in dynamic multi-tasking environments, a suitable multi-robot task-allocation (MRTA) solution is required. This must be scalable to variable number of robots and tasks. Recent studies show that biology-inspired self-organized approaches can effectively handle task-allocation in large multi-robot systems. However most existing MRTA approaches have overlooked the role of different communication and sensing strategies found in selfregulated biological societies. This dissertation proposes to solve the MRTA problem using a set of previously published generic rules for division of labour derived from the observation of ant,human and robotic social systems. The concrete form of these rules, the attractive field model (AFM), provides sufficient abstraction to local communication and sensing which is uncommon in existing MRTA solutions. This dissertation validates the effectiveness of AFM to address MRTA using two bio-inspired communication and sensing strategies: "global sensing - no communication" and "local sensing - local communication". The former is realized using a centralized communication system and the latter is emulated under a peer-topeer local communication scheme. They are applied in a manufacturing shop-floor scenario using 16 e-puck robots. A robotic interpretation of AFM is presented that maps the generic parameters of AFM to the properties of a manufacturing shopfloor. A flexible multi-robot control architecture, hybrid event-driven architecture on D-Bus, has been outlined which uses the state-of-the-art D-Bus interprocess communication to integrate heterogeneous software components. Based-on the organization of task-allocation, communication and interaction among robots, a novel taxonomy of MRTA solutions has been proposed to remove the ambiguities found in existing MRTA solutions. Besides, a set of domainindependent metrics, e.g., plasticity, task-specialization and energy usage, has been formalized to compare the performances of the above two strategies. The presented comparisons extend our general understanding of the role of information exchange strategies to achieve the distributed task-allocations among various social groups.
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Gage, Aaron. "Multi-robot task allocation using affect." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000465.
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Schneider, E. "Mechanism selection for multi-robot task allocation." Thesis, University of Liverpool, 2018. http://livrepository.liverpool.ac.uk/3018369/.
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There is increasing interest in fielding multi-robot teams for applications such as search and rescue, warehouse automation, and delivery of consumer goods. Task allocation is an important problem to solve in such multi-robot settings. Given a mission that can be decomposed into discrete tasks, the Multi-Robot Task Allocation (MRTA) problem looks for an assignment of tasks to robots that ultimately results in efficient execution of the mission. There is a range of approaches to this optimisation problem, from centralised solvers to fully distributed methods that involve no explicit coordination between team members. Somewhere in the middle of this range lie market-based approaches, where tasks can be treated as goods, robots as "buyers" who can compute and express their own preferences for tasks in a virtual marketplace, and some clearing mechanism exists to match tasks to robots according to these preferences. The most common type of market-based mechanism for multi-robot task allocation is an auction, in which tasks are announced to the team, robots compute and place bids that encode some measure of cost or utility of performing the tasks, and tasks are awarded to robots over a number of rounds, according to the particular rules of the mechanism. Many different auction mechanisms exist, and they vary in the trade-offs that they make between computation time and space on the one hand, and performance of the execution of the mission on the other. In addition, the performance that results from a mechanism's allocation can be greatly affected by properties of task environments---the spatial and temporal arrangements of tasks, as well as other properties like precedence constraints, whether tasks require the simultaneous cooperation of multiple robots, and so on---in which it is employed. A simple mechanism that is inexpensive to compute and scales well may perform well in some environments, but not in others. The work presented in this thesis focuses on this relationship between auction-based task allocation mechanisms and properties of task environments, with the goal of developing a method of selecting, from a portfolio, a mechanism that is appropriate for a given task environment. The first part of this work is an empirical performance evaluation of a range of mechanisms employed in a series of environments of increasing complexity. The second part of this work uses results from this evaluation to develop and train a data-driven method of mechanism selection using properties of environments that can be measured at the start of a mission. The results show that, under certain conditions, this method of mechanism selection can lead to significant performance improvements compared to using a single mechanism alone.
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Das, Gautham Panamoottil. "Task allocation strategies for multi-robot systems." Thesis, Ulster University, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667759.
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Sung, Cynthia Rueyi. "Data-driven task allocation for multi-robot deliveries." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/84717.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 93-97).
In this thesis, we present a distributed task allocation system for a team of robots serving queues of tasks in an environment. We consider how historical information about such a system's performance could be used to improve future allocations. Our model is representative of a multi-robot mail delivery service, in which teams of robots would have to cooperate to pick up and deliver packages in an environment. We provide a framework for task allocation, planning, and control of the system and analyze task switching as a method for improving a task allocation as the system is running. We first treat a system where robots exchange tasks as they encounter each other in the environment. We consider both cases where the number of robots matches the number of task queues being served and where it does not. Most importantly, for situations where an optimal task switching policy would be too computationally expensive, we provide heuristics that nonetheless guarantee task completion. Our simulations show that our heuristics also generally lower the costs of task completion. We incorporate historical data about system performance by looking at a spatial allocation of tasks to robots in the system. We propose an algorithm for partitioning the environment into regions of equal workload for the robots. In order to overcome communication constraints, we introduce hubs, locations where robots can pass tasks to each other. We simulate the system with this additional infrastructure and compare its performance to that without hubs. We find that hubs can significantly improve performance when the task queues themselves follow some spatial structure.
by Cynthia Rueyi Sung.
S.M.
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AL-Buraiki, Omar S. M. "Specialized Agents Task Allocation in Autonomous Multi-Robot Systems." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41504.
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With the promise to shape the future of industry, multi-agent robotic technologies have the potential to change many aspects of daily life. Over the coming decade, they are expected to impact transportation systems, military applications such as reconnaissance and surveillance, search-and-rescue operations, or space missions, as well as provide support to emergency first responders.
Motivated by the latest developments in the field of robotics, this thesis contributes to the evolution of the future generation of multi-agent robotic systems as they become smarter, more accurate, and diversified in terms of applications. But in order to achieve these goals, the individual agents forming cooperative robotic systems need to be specialized in what they can accomplish, while ensuring accuracy and preserving the ability to perform diverse tasks.
This thesis addresses the problem of task allocation in swarm robotics in the specific context where specialized capabilities of the individual agents are considered. Based on the assumption that each individual agent possesses specialized functional capabilities and that the expected tasks, which are distributed in the surrounding environment, impose specific requirements, the proposed task allocation mechanisms are formulated in two different spaces. First, a rudimentary form of the team members’ specialization is formulated as a cooperative control problem embedded in the agents’ dynamics control space. Second, an advanced formulation of agents’ specialization is defined to estimate the individual agents’ task allocation probabilities in a dedicated specialization space, which represents the core contribution of this thesis to the advancement and practice in the area of swarm robotics.
The original task allocation process formulated in the specialization space evolves through four stages of development. First, a task features recognition stage is conceptually introduced to leverage the output of a sensing layer embedded in robotic agents to drive the proposed task allocation scheme. Second, a matching scheme is developed to best match each agent’s specialized capabilities with the corresponding detected tasks. At this stage, a general binary definition of agents’ specialization serves as the basis for task-agent association. Third, the task-agent matching scheme is expanded to an innovative probabilistic specialty-based task-agent allocation framework to generalize the concept and exploit the potential of agents’ specialization consideration. Fourth, the general framework is further refined with a modulated definition of the agents’ specialization based on their mechanical, physical structure, and embedded resources. The original framework is extended and a prioritization layer is also introduced to improve the system’s response to complex tasks that are characterized based on the recognition of multiple classes.
Experimental validation of the proposed specialty-based task allocation approach is conducted in simulation and on real-world experiments, and the results are presented and discussed in light of potential applications to demonstrate the effectiveness and efficiency of the proposed framework.
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KARAMI, HOSSEIN. "Task planning and allocation for multi-agent collaborative robot systems." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1083925.
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In this thesis we address the problem of human robot interaction in industrial environments from collaboration perspective. This thesis, in particular, focuses on introducing novel
frameworks for coordination of heterogeneous teams made of humans and robots that collaboratively aim to reach to a common goal.
In the last decade, robots has received enormous attentions for being employed in both industrial environments and workplaces. Thin is mainly because of a number of reasons: (I)
the shift of mass production industries toward autonomous industry units, (II) huge amount of financial and scientific investments on robotics, and (III) substitution of humans with robots to accomplish hazardous and stressful tasks. However Due to the limited cognitive knowledge and reasoning of the robots in accomplishing complex operations, they still are not able to operate in a fully autonomous fashion and independently from their human counterparts. Therefore presence of human operators, as a complementary counterparts, in workplaces becomes fundamental for robots to become utterly safe, reliable and operative. The goal of this thesis is to design and implement a framework whereby humans and/or robots can together play a complementary role, while applying their individual skills to accomplish a
task. Human-robot collaboration (HRC) is defined as the purposeful interaction among humans and robots in a shared space, and it is aimed at a common goal. The design of such framework for HRC problems, requires to satisfy many requirements from which flexibility, adaptability and safety, are the primary characteristics of such framework.
In this thesis we mainly focus on multi-agent robot systems task allocation and planning. We consider two main aspects in defining our objectives: on one hand we investigate on
HRC, and implement alternative frameworks to model and study collaborations in industrial scenarios considering various roles of humans in coordination and collaboration with robots.
On the other hand, the presence of humans is neglected and it is assumed that robots are able to fully precept the environment independently from human cognitive support, as this can be the case of future where Artificial intelligence might substitute the skills of humans. To model a HRC scenario, a smart framework is required to start, coordinate and terminate
a collaborative process. This framework, in particular has to be aware of agents and their types, determine their responsibilities and roles, be aware of their physical structure, define
the logical relationship among the agent and handle the collaboration process fluently. In this thesis, to address the framework described above, we propose different frameworks
and evaluate their effectiveness in solving HRC problems. To formulate task planning and allocation problem , we introduce and implement three variants of AND/OR graphs,
namely, c-layer AND/OR graphs, Branched AND/OR graphs, and Iteratively deepened AND/OR graphs. The first two variants aim at addressing the problem of task allocation
among humans and collaborative robots in object defect inspection (ODI) scenarios in HRC context. Instead, the third variant targets Task and motion planning (TAMP) problems for
heterogeneous robots. TAMP problems, compared to HRC problem, is not only responsible for allocating task among agents at higher-level, but also at lower level, it plans motions for
agents and ultimately, interconnects higher levels of task planning to lower levels of motion planning and control to achieve a complete planning framework. To validate the applicability
and scalability of our proposed frameworks, we design and implement various real-world and simulation experiments and we also evaluate their effectiveness in terms of achieving
desired objectives, and quantitatively with other available methods in the literature.
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Sun, Dali [Verfasser], and Bernhard [Akademischer Betreuer] Nebel. "Adaptive task allocation, localization and motion planning for the multi-robot system." Freiburg : Universität, 2017. http://d-nb.info/114157571X/34.
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Dutia, Dharini. "Multi-Robot Task Allocation and Scheduling with Spatio-Temporal and Energy Constraints." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-theses/1298.
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Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot's energy level, and the time left to task inactivation.
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Luo, Lingzhi. "Distributed Algorithm Design for Constrained Multi-robot Task Assignment." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/426.
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The task assignment problem is one of the fundamental combinatorial optimization problems. It has been extensively studied in operation research, management science, computer science and robotics. Task assignment problems arise in various applications of multi-robot systems (MRS), such as environmental monitoring, disaster response, extraterrestrial exploration, sensing data collection and collaborative autonomous manufacturing. In these MRS applications, there are realistic constraints on robots and tasks that must be taken into account both from the modeling perspective and the algorithmic perspective. From the modeling aspect, such constraints include (a) Task group constraints: where tasks form disjoint groups and each robot can be assigned to at most one task in each group. One example of the group constraints comes from tightly-coupled tasks, where multiple micro tasks form one tightly-coupled macro task and need multiple robots to perform each simultaneously. (b) Task deadline constraints: where tasks must be assigned to meet their deadlines. (c) Dynamically-arising tasks: where tasks arrive dynamically and the payoffs of future tasks are unknown. Such tasks arise in scenarios like searchrescue, where new victims are found dynamically. (d) Robot budget constraints: where the number of tasks each robot can perform is bounded according to the resource it possesses (e.g., energy). From the solution aspect, there is often a need for decentralized solution that are implemented on individual robots, especially when no powerful centralized controller exists or when the system needs to avoid single-point failure or be adaptive to environmental changes. Most existing algorithms either do not consider the above constraints in problem modeling, are centralized or do not provide formal performance guarantees. In this thesis, I propose methods to address these issues for two classes of problems, namely, the constrained linear assignment problem and constrained generalized assignment problem. Constrained linear assignment problem belongs to P, while constrained generalized assignment problem is NP-hard. I develop decomposition-based distributed auction algorithms with performance guarantees for both problem classes. The multi-robot assignment problem is decomposed into an optimization problem for each robot and each robot iteratively solving its own optimization problem leads to a provably good solution to the overall problem. For constrained linear assignment problem, my approaches provides an almost optimal solution. For constrained generalized assignment problem, I present a distributed algorithm that provides a solution within a constant factor of the optimal solution. I also study the online version of the task allocation problem with task group constraints. For the online problem, I prove that a repeated greedy version of my algorithm gives solution with constant factor competitive ratio. I include simulation results to evaluate the average-case performance of the proposed algorithms. I also include results on multi-robot cooperative package transport to illustrate the approach.
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Bhal, Siddharth. "Fog computing for robotics system with adaptive task allocation." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78723.
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The evolution of cloud computing has finally started to affect robotics.
Indeed, there have been several real-time cloud applications making their way into robotics as of late. Inherent benefits of cloud robotics include providing virtually infinite
computational power and enabling collaboration of a multitude of connected devices.
However, its drawbacks include higher latency and overall higher energy consumption.
Moreover, local devices in proximity incur higher latency when communicating among themselves via the cloud. At the same time, the cloud is a single point of failure in the network. Fog Computing is an extension of the cloud computing paradigm providing data, compute, storage and application services to end-users on a so-called edge layer. Distinguishing characteristics are its support for mobility and dense geographical distribution. We propose to study the implications of applying fog computing concepts in robotics by developing a middle-ware solution for Robotic Fog Computing Cluster solution for enabling adaptive distributed computation in heterogeneous multi-robot systems interacting with the Internet of Things (IoT). The developed middle-ware has a modular plug-in architecture based on micro-services and facilitates communication of IOT devices with the multi-robot systems.
In addition, the developed middle-ware solutions support different load balancing or task allocation algorithms. In particular, we establish that we can enhance the performance of distributed system by decreasing overall system latency by using already established multi-criteria decision-making algorithms like TOPSIS and TODIM with naive Q-learning and with Neural Network based Q-learning.Master of Science
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Liu, Chun [Verfasser]. "Multi-Robot Task Allocation for Inspection Problems with Cooperative Tasks Using Hybrid Genetic Algorithms / Chun Liu." Kassel : Universitätsbibliothek Kassel, 2014. http://d-nb.info/1060773058/34.
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Viguria, Jimenez Luis Antidio. "Distributed Task Allocation Methodologies for Solving the Initial Formation Problem." Thesis, Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24731.
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Mobile sensor networks have been shown to be a powerful tool for enabling a number of activities that require recording of environmental parameters at various spatial and temporal distributions. These mobile sensor networks could be implemented using a team of robots, usually called robotic sensor networks.
This type of sensor network involves the coordinated control of multiple robots to achieve specific measurements separated by varied distances. In most formation measurement applications, initialization involves identifying a number of interesting sites to which mobility platforms, instrumented with a variety of sensors, are tasked. This process of determining which
instrumented robot should be tasked to which location can be viewed as solving the task allocation problem.
Unfortunately, a centralized approach does not fit in this type of application due to the fault tolerance requirements. Moreover, as the size of the network grows, limitations in bandwidth severely limits the possibility of conveying and using global information. As such, the utilization of decentralized techniques for forming new sensor topologies and configurations is a highly desired quality of robotic sensor networks.
In this thesis, several distributed task allocation algorithms will be explained and compared in different scenarios. They are based on a market approach since our interest is not only to obtain a feasible solution, but also an efficient one. Also, an analysis of the efficiency of those algorithms using probabilistic techniques will be explained.
Finally, the task allocation algorithms will be implemented on a real system consisted of a team of six robots and integrated in a complete robotic system that considers obstacle avoidance and path planning. The results will be validated in both simulations and real experiments.
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Sheth, Rohit S. "A Decentralized Strategy for Swarm Robots to Manage Spatially Distributed Tasks." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/400.
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Large-scale scenarios such as search-and-rescue operations, agriculture, warehouse, surveillance, and construction consist of multiple tasks to be performed at the same time. These tasks have non-trivial spatial distributions. Robot swarms are envisioned to be efficient, robust, and flexible for such applications. We model this system such that each robot can service a single task at a time; each task requires a specific number of robots, which we refer to as 'quota'; task allocation is instantaneous; and tasks do not have inter- dependencies. This work focuses on distributing robots to spatially distributed tasks of known quotas in an efficient manner. Centralized solutions which guarantee optimality in terms of distance travelled by the swarm exist. Although potentially scalable, they require non-trivial coordination; could be computationally expensive; and may have poor response time when the number of robots, tasks and task quotas increase. For a swarm to efficiently complete tasks with a short response time, a decentralized approach provides better parallelism and scalability than a centralized one. In this work, we study the performance of a weight-based approach which is enhanced to include spatial aspects. In our approach, the robots share a common table that reports the task locations and quotas. Each robot, according to its relative position with respect to task locations, modifies weights for each task and randomly chooses a task to serve. Weights increase for tasks that are closer and have high quota as opposed to tasks which are far away and have low quota. Tasks with higher weights have a higher probability of being selected. This results in each robot having its own set of weights for all tasks. We introduce a distance- bias parameter, which determines how sensitive the system is to relative robot-task locations over task quotas. We focus on evaluating the distance covered by the swarm, number of inter- task switches, and time required to completely allocate all tasks and study the performance of our approach in several sets of simulated experiments.
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Hayakawa, Tomohiro. "Adaptation of a group to various environments through local interactions between individuals based on estimated global information." Kyoto University, 2020. http://hdl.handle.net/2433/259039.
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付記する学位プログラム名: グローバル生存学大学院連携プログラムKyoto University (京都大学)
0048
新制・課程博士
博士(工学)
甲第22771号
工博第4770号
新制||工||1746(附属図書館)
京都大学大学院工学研究科機械理工学専攻
(主査)教授 松野 文俊, 教授 椹木 哲夫, 教授 泉田 啓
学位規則第4条第1項該当
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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.
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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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Guerrero, Sastre José. "Nuevas metodologías para la asignación de tareas y formación de coaliciones en sistemas multi-robot." Doctoral thesis, Universitat de les Illes Balears, 2011. http://hdl.handle.net/10803/32147.
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Este trabajo analiza la idoneidad de dos de los principales métodos de asignación de tareas en entornos con restricciones temporales. Se pondrá de manifiesto que ambos tipos de mecanismos presentan carencias para tratar tareas con deadlines, especialmente cuando los robots han de formar coaliciones. Uno de los aspectos a los que esta tesis dedica mayor atención es la predicción del tiempo de ejecución, que depende, entre otros factores, de la interferencia física entre robots. Este fenómeno no se ha tenido en cuenta en los mecanismos actuales de asignación basados en subastas.
Así, esta tesis presenta el primer mecanismo de subastas para la creación de coaliciones que tiene en cuenta la interferencia entre robots. Para ello, se ha desarrollado un modelo de predicción del tiempo de ejecución y un nuevo paradigma llamado subasta doble. Además, se han propuesto nuevos mecanismos basados en swarm
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Forsslund, Patrik, and Simon Monié. "MULTI-DRONE COLLABORATION FOR SEARCH AND RESCUE MISSIONS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-54439.
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Unmanned Aerial Vehicle (UAV), also called drones, are used for Search And Rescue (SAR) missions, mainly in the form of a pilot manoeuvring a single drone. However, the increase in labour to cover larger areas quickly would result in a very high cost and time spent per rescue operation. Therefore, there is a need for an easy to use, low-cost, and highly autonomous swarm of drones for SAR missions where the detection and rescue times are kept to a minimum. In this thesis, a Subsumption-based architecture is proposed, which combines multiple behaviours to create more complex behaviours. An investigation of (1) what are the critical aspects of controlling a swarm of drones, (2) how can a combination of different behavioural algorithms increase the performance of a swarm of drones, and (3) what benchmarks are necessary when evaluating the fitness of the behavioural algorithms. The proposed architecture was simulated in AirSim using the SimpleFlight flight controller through experiments that evaluated the individual layers and missions that simulated real-life scenarios. The results validate the modularity and reliability of the architecture, where the architecture has the potential for improvements in future iterations. For the search area of 400×400meters, the swarm consistently produced an average area coverage of at least 99.917% and found all the missing people in all missions, with the slowest average being 563 seconds. Compared to related work, the result produced similar or better times when scaled to the same proportions and higher area coverage. As comparisons of results in SAR missions can be difficult, the introduction of Active time can serve as a benchmark for others in future swarm performance measurements.
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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’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." Electronic Thesis or Diss., Université de Lorraine, 2013. http://www.theses.fr/2013LORR0261.
Full textAbstract:
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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Choudhury, Bibhuti Bhusan. "Task Allocation Strategies in Multi-Robot Environment." Thesis, 2009. http://ethesis.nitrkl.ac.in/2773/1/B.B.Choudhury_Ph.D_Thesis_50603003.pdf.
Full textAbstract:
Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. A robot team can accomplish a given task more quickly than a single agent by executing them concurrently. A team can also make effective use of specialists designed for a single purpose rather than requiring that a single robot be a generalist. Multirobot coordination, however, is a complex problem. An empirical study is described in the thesis that sought general guidelines for task allocation strategies. Different strategies are identified, and demonstrated in the multi-robot environment.Robot selection is one of the critical issues in the design of robotic workcells. Robot selection for an application is generally done based on experience, intuition and at most using the kinematic considerations like workspace, manipulability, etc. This problem has become more difficult in recent years due to increasing complexity, available features, and facilities offered by different robotic products. A systematic procedure is developed for selection of robot manipulators based on their different pertinent attributes. The robot selection procedure allows rapid convergence from a very large number of candidate robots to a manageable shortlist of potentially suitable robots. Subsequently, the selection procedure proceeds to rank the alternatives in the shortlist by employing different attributes based specification methods. This is an attempt to create exhaustive procedure by identifying maximum possible number of attributes for robot manipulators.Availability of large number of robot configurations has made the robot workcell designers think over the issue of selecting the most suitable one for a given set of operations. The process of selection of the appropriate kind of robot must consider the various attributes of the robot manipulator in conjunction with the requirement of the various operations for accomplishing the task. The present work is an attempt to develop a systematic procedure for selection of robot based on an integrated model encompassing the manipulator attributes and manipulator requirements.
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Sullivan, Nicholas David. "Task Allocation and Collaborative Localisation in Multi-Robot Systems." Thesis, 2019. http://hdl.handle.net/2440/120578.
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To utilise multiple robots, it is fundamental to know what they should do, called task allocation, and to know where the robots are, called localisation. The order that tasks are completed in is often important, and makes task allocation difficult to solve (40 tasks have 1047 different ways of completing them). Algorithms in literature range from fast methods that provide reasonable allocations, to slower methods that can provide optimal allocations. These algorithms work well for systems with identical robots, but do not utilise robot differences for superior allocations when robots are non-identical. They also can not be applied to robots that can use different tools, where they must consider which tools to use for each task. Robot localisation is performed using sensors which are often assumed to always be available. This is not the case in GPS-denied environments such as tunnels, or on long-range missions where replacement sensors are not readily available. A promising method to overcome this is collaborative localisation, where robots observe one another to improve their location estimates. There has been little research on what robot properties make collaborative localisation most effective, or how to tune systems to make it as accurate as possible. Most task allocation algorithms do not consider localisation as part of the allocation process. If task allocation algorithms limited inter-robot distance, collaborative localisation can be performed during task completion. Such an algorithm could equally be used to ensure robots are within communication distance, and to quickly detect when a robot fails. While some algorithms for this exist in literature, they provide a weak guarantee of inter-robot distance, which is undesirable when applied to real robots. The aim of this thesis is to improve upon task allocation algorithms by increasing task allocation speed and efficiency, and supporting robot tool changes. Collaborative localisation parameters are analysed, and a task allocation algorithm that enables collaborative localisation on real robots is developed. This thesis includes a compendium of journal articles written by the author. The four articles forming the main body of the thesis discuss the multi-robot task allocation and localisation research during the author’s candidature. Two appendices are included, representing conference articles written by the author that directly relate to the thesis.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2019
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Liu, Lantao. "Linear Sum Assignment Algorithms for Distributed Multi-robot Systems." Thesis, 2013. http://hdl.handle.net/1969.1/149316.
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Multi-robot task assignment (allocation) involves assigning robots to tasks in order to optimize the entire team’s performances. Until now, one of the most useful non-domain-specific ways to coordinate multi-robot systems is through task allocation mechanisms. This dissertation addresses the classic task assignment problems in which robots and tasks are eventually matched by forming a one-to-one mapping, and their overall performances (e.g., cost, utility, and risk) can be linearly summed.
At a high level, this research emphasizes two facets of the multi-robot task assignment, including (1) novel extensions from classic assignment algorithms, and (2) completely newly designed task allocation methods with impressive new features.
For the former, we first propose a strongly polynomial assignment sensitivity analysis algorithm as well as a means to measure the assignment uncertainties; after that we propose a novel method to address problems of multi-robot routing and formation morphing, the trajectories of which are obtained from projections of augmenting paths that reside in a new three-dimensional interpretation of embedded matching graphs.
For the latter, we present two optimal assignment algorithms that are distributable and suitable for multi-robot task allocation problems: the first one is an anytime assignment algorithm that produces non-decreasing assignment solutions along a series of task-swapping operations, each of which updates the assignment configurations and thus can be interrupted at any moment; the second one is a new market-based algorithm with a novel pricing policy: in contrast to the buyers’ “selfish” bidding behaviors in conventional auction/market-based approaches, we employ a virtual merchant to strategically escalate market prices in order to reach a state of equilibrium that satisfies both the merchant and buyers. Both of these newly developed assignment algorithms have a strongly polynomial running time close to the benchmark algorithms but can be easily decentralized in terms of computation and communication.
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chen, wei-hsiu, and 陳維修. "The Study on the Simulation of Improved Ant Colony Algorithm for Multi-Robot Path Planning and Task Allocation." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/16330973865565893467.
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碩士國立臺灣師範大學
工業教育學系
98
The invention of robots is to replace overwhelming work for human faculty in hazardous conditions. With the improvement of robot function, it makes the working style come from single robot completing a task independently to multi-robot completing a complex task. For the latter case, the task allocation and path planning should be considered in depth to optimize performance of working group. The algorithm purposed for task allocation and path planning for multi- robot is called “Ant Colony Algorithm” by a research group in China in 2008. They used pheromone (strength of trail) of past ant to define optimal route for the next ant. But some ants may not be able to follow the optimal route due to their local optimization and not global optimization. This thesis purposed a modified method to find the best route for any ant in the group and they will avoid collision between each other when they are moving. The experimental results show that any ant (robot) can move on optimal route according to global optimal computation and avoids collision according to local optimal computation. Its performance is better than former Ant Colony Algorithm. Therefore, it can be used for multi-robot task allocation and path planning in the case of static environment.
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Peasgood, Mike. "Cooperative Navigation for Teams of Mobile Robots." Thesis, 2007. http://hdl.handle.net/10012/3575.
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Teams of mobile robots have numerous applications, such as space exploration,
underground mining, warehousing, and building security. Multi-robot teams can provide a number of practical benefits in such applications, including simultaneous presence in multiple locations, improved system performance, and greater robustness and redundancy compared to individual robots. This thesis addresses three aspects of coordination and navigation for teams of mobile robots: localization, the estimation of the position of each robot in the environment; motion planning, the process of finding collision-free trajectories through the environment; and task allocation, the selection of appropriate goals to be assigned to each robot. Each of these topics are
investigated in the context of many robots working in a common environment.
A particle-filter based system for cooperative global localization is presented.
The system combines the sensor data from three robots, including measurements of the distances between robots, to cooperatively estimate the global position of each robot in the environment. The method is developed for a single triad of robots, then extended to larger groups of robots. The algorithm is demonstrated in a simulation of robots equipped with only simple range sensors, and is shown to successfully achieve global localization of robots that are unable to localize using only their own local sensor data.
Motion planning is investigated for large teams of robots operating in tunnel and corridor environments, where coordinated planning is often required to avoid collision or deadlock conditions. A complete and scalable motion planning algorithm is presented and evaluated in simulation with up to 150 robots. In contrast to popular decoupled approaches to motion planning (which cannot guarantee a solution), this algorithm uses a multi-phase approach to create and maintain obstacle-free paths through a graph representation of the environment. The resulting plan is a set of collision-free trajectories, guaranteeing that every robot will reach its goal.
The problem of task allocation is considered in the same type of tunnel and corridor environments, where tasks are defined as locations in the environment that must be visited by one of the robots in the team. To find efficient solutions to the task allocation problem, an optimization approach
is used to generate potential task assignments, and select the best solution.
The multi-phase motion planner is applied within this system as an efficient method of evaluating potential task assignments for many robots in a large environment. The algorithm is evaluated in simulations with up to 20 robots in a map of large underground mine.
A real-world implementation of 3 physical robots was used to demonstrate the implementation of the multi-phase motion planning and task allocation systems. A centralized motion planning and task allocation system was developed, incorporating localization and time-dependent trajectory tracking on the robot processors, enabling cooperative navigation in a shared hallway environment.