Academic literature on the topic 'Multi-Robot systems (MRS)'
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Journal articles on the topic "Multi-Robot systems (MRS)"
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G.V Chalapathi Rao, Kandhyanam Mahesh, and Maheshwaram Shiva. "Gradient Based Routing Protocol For Modular Robotics." international journal of engineering technology and management sciences 7, no. 3 (2023): 235–40. http://dx.doi.org/10.46647/ijetms.2023.v07i03.030.
Full textAbstract:
Advancements in microprocessor-based systems have revolutionized robotics, enabling single-robot and multi-robot systems (MRS) to excel in various applications such as search and rescue, forest fire detection, mining, and disaster management. MRS systems amplify robot capabilities, enabling complex tasks and distributed operations. Effective communication between robots is crucial for optimal performance. This paper explores MRS architectures, emphasizing networking issues and required services for enhanced efficiency. It compares MRS systems to mobile ad hoc networks (MANETs), analyzes robot-to-robot (R2R) and robot-to-infrastructure (R2I) communication links, and identifies protocols applicable at different levels of the MRS hierarchy.
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Jawhar, Imad, Nader Mohamed, Jie Wu, and Jameela Al-Jaroodi. "Networking of Multi-Robot Systems: Architectures and Requirements." Journal of Sensor and Actuator Networks 7, no. 4 (November 30, 2018): 52. http://dx.doi.org/10.3390/jsan7040052.
Full textAbstract:
A large number of advancements have taken place in microprocessor-based systems leading to significantly more processing, memory, storage, sensing, actuating, recognition, controlling and communication capabilities. Robotics is one of the areas that have benefited a lot from these advancements. Many important and useful applications for single-robot and multi-robot systems (MRS) have emerged. Such applications include search and rescue, detection of forest fires, mining, construction, disaster management, and many more. MRS systems greatly enhance the capabilities and effectiveness of today’s robots. They extend the robotic system capabilities by increasing the ability to perform more complex tasks and allow performance of inherently distributed ones. In addition, they increase parallelism, enhance robustness, and improve system reliability. However, to perform their tasks in an effective manner, communication between the individual robots becomes an essential component. In this paper, we discuss the various types and architectures of MRS systems and focus on the networking issues, and services that are required to enable MRS systems to be more efficient in performing their roles in their respective applications. We also identify the similarities and differences between mobile ad hoc networks (MANETs) and MRS systems, analyze robot-to-robot (R2R) and robot-to-infrastructure (R2I) communication links, and identify the protocols that can be used at the various levels in the MRS hierarchy.
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Khalastchi, Eliahu, and Meir Kalech. "Fault Detection and Diagnosis in Multi-Robot Systems: A Survey." Sensors 19, no. 18 (September 18, 2019): 4019. http://dx.doi.org/10.3390/s19184019.
Full textAbstract:
The use of robots has increased significantly in the recent years; rapidly expending to numerous applications. These sophisticated machines are susceptible to different types of faults that might endanger the robot or its surroundings. These faults must be detected and diagnosed in time to allow continual operation. The field of Fault Detection and Diagnosis (FDD) has been studied for many years. This research has given birth to many approaches that are applicable to different types of physical machines. However, the domain of robotics poses unique requirements that challenge traditional FDD approaches. The study of FDD for robotics is relatively new; only few surveys were presented. These surveys have focused on the single robot scenario. To the best of our knowledge, there is no survey that focuses on FDD for Multi-Robot Systems (MRS). In this paper we set out to fill this gap. This paper provides detailed insights to the world of FDD for MRS. We first describe how different attributes of MRS pose different challenges for FDD. With respect to these challenges, we survey different FDD approaches applicable for MRS. We conclude with a description of research opportunities in this field. With these contributions it is the authors’ intention to provide detailed insights to the world of FDD for MRS.
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CHOUDHURY, B. B., and B. B. BISWAL. "ALTERNATIVE METHODS FOR MULTI-ROBOT TASK ALLOCATION." Journal of Advanced Manufacturing Systems 08, no. 02 (December 2009): 163–76. http://dx.doi.org/10.1142/s0219686709001717.
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One of the most important aspects in the design of multi-robot systems (MRS) is the allocation of tasks among the robots in a productive and efficient manner. This paper presents an empirical study on task allocation strategies in multirobot environment. In general, optimal solutions are found through an exhaustive search, but because there are n × m ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible with increased number of tasks. Task allocation methodologies for multirobot systems are developed by considering their capability in terms of time and space. The present work adopts a two-phase methodology to allocate tasks optimally amongst the candidate robots. The allocation cost of the robots is determined during the first phase and alternate algorithms are used in the second phase for optimizing the allocation. The work considers systems of practical sizes and the results obtained through this are helpful in recommending appropriate techniques to the users of MRS for increasing producibility and robot utilization. Three different approaches using Linear programming, Hungarian Algorithm and Knapsack Algorithm are presented and their results are analyzed for the suitability of the methods for an allocation problem. Simulation results are presented and compared for the benefit of the users.
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DRAGOMIR, OTILIA ELENA. "MODELLING AND SIMULATION OF DISTRIBUTED SYSTEMS USING INTELLIGENT MULTI-AGENTS." Journal of Science and Arts 22, no. 2 (June 30, 2022): 471–82. http://dx.doi.org/10.46939/j.sci.arts-22.2-a19.
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Among the challenges in the field of the paper we have identified the necessity of control methods and tools of mobile robots (MRS) equipped with robot manipulator (RM) serving assembly/ disassembly mechatronic lines (A/DML).The framework of the work reported here is represented by SMART&ASTI A/DML served by two MRs with RM, working collaboratively and the goal is development of a multi-agent system able control interactively trajectories of MRs, working collaboratively to serve the A/DML, by avoiding the collisions between them. The advantage offered by the proposed solution consists in graphical representation of the trajectory of MRs working collaboratively, as well as the current status of them, while the users are able to interact intuitively with the MRs trough the proposed GUI The added value of the paper consists in the implementation of multi-agents systems in a complex A/DML served by two MRs equipped with RM, working collaboratively, the increased autonomy in communication between the entities of entire system, and the adaptive control of MRs trajectories.
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Wagdy, Ahmed, and Alaa Khamis. "Adaptive Group Formation in Multirobot Systems." Advances in Artificial Intelligence 2013 (October 21, 2013): 1–15. http://dx.doi.org/10.1155/2013/692658.
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Multirobot systems (MRSs) are capable of solving task complexity, increasing performance in terms of maximizing spatial/temporal/radio coverage or minimizing mission completion time. They are also more reliable than single-robot systems as robustness is increased through redundancy. Many applications such as rescue, reconnaissance, and surveillance and communication relaying require the MRS to be able to self-organize the team members in a decentralized way. Group formation is one of the benchmark problems in MRS to study self-organization in these systems. This paper presents a hybrid approach to group formation problem in multi-robot systems. This approach combines the efficiency of the cellular automata as finite state machine, the interconnectivity of the virtual grid and its bonding technique, and last but not least the decentralization of the adaptive dynamic leadership.
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Suman Sangwan, Vandana Dabass,. "Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (April 25, 2024): 3895–901. http://dx.doi.org/10.17762/ijritcc.v11i9.10484.
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Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field.
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HSU, HARRY CHIA-HUNG, and ALAN LIU. "APPLYING A TAXONOMY OF FORMATION CONTROL IN DEVELOPING A ROBOTIC SYSTEM." International Journal on Artificial Intelligence Tools 16, no. 04 (August 2007): 565–82. http://dx.doi.org/10.1142/s0218213007003436.
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Designing cooperative multi-robot systems (MRS) requires expert knowledge both in control and artificial intelligence. Formation control is an important research within the research field of MRS. Since many researchers use different ways in approaching formation control, we try to give a taxonomy in order to help researchers design formation systems in a systematical way. We can analyze formation structures in two categories: control abstraction and robot distinguishability. The control abstraction can be divided into three layers: formation shape, reference type, and robotic control. Furthermore, robots can be classified as anonymous robots or identification robots depending on whether robots are distinguishable according to their inner states. We use this taxonomy to analyze some ground-based formation systems and to state current challenges of formation control. Such information becomes the design know-how in developing a formation system, and a case study of designing a multi-team formation system is introduced to demonstrate the usefulness of the taxonomy.
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Schweim, Anne, Marvin Zager, Marie Schweim, Alexander Fay, and Joachim Horn. "Unmanned vehicles on the rise: a review on projects of cooperating robot teams." at - Automatisierungstechnik 72, no. 1 (January 1, 2024): 3–14. http://dx.doi.org/10.1515/auto-2022-0153.
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Abstract Teams consisting of cooperating mobile robots are able to carry out tasks faster and more effectively than individual robots. Previous research projects have focused on teams of homogeneous robots in particular, whereas heterogeneous robots are becoming increasingly important. In this paper, the fundamentals of both homogeneous and heterogeneous robot teams are examined. In this context, not only key fundamentals are distinguished from each other, but also potential applications and advantages of multi-robot systems (MRS) are shown. The rapid development of autonomous MRS is shown by means of exemplarily selected research projects, whereby the individual approaches of these projects are systematically compared and evaluated. Finally, an outlook on remaining challenges in the field of autonomous heterogeneous MRS is given.
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Liu, Yang, and Jiankun Li. "Runtime Verification-Based Safe MARL for Optimized Safety Policy Generation for Multi-Robot Systems." Big Data and Cognitive Computing 8, no. 5 (May 16, 2024): 49. http://dx.doi.org/10.3390/bdcc8050049.
Full textAbstract:
The intelligent warehouse is a modern logistics management system that uses technologies like the Internet of Things, robots, and artificial intelligence to realize automated management and optimize warehousing operations. The multi-robot system (MRS) is an important carrier for implementing an intelligent warehouse, which completes various tasks in the warehouse through cooperation and coordination between robots. As an extension of reinforcement learning and a kind of swarm intelligence, MARL (multi-agent reinforcement learning) can effectively create the multi-robot systems in intelligent warehouses. However, MARL-based multi-robot systems in intelligent warehouses face serious safety issues, such as collisions, conflicts, and congestion. To deal with these issues, this paper proposes a safe MARL method based on runtime verification, i.e., an optimized safety policy-generation framework, for multi-robot systems in intelligent warehouses. The framework consists of three stages. In the first stage, a runtime model SCMG (safety-constrained Markov Game) is defined for the multi-robot system at runtime in the intelligent warehouse. In the second stage, rPATL (probabilistic alternating-time temporal logic with rewards) is used to express safety properties, and SCMG is cyclically verified and refined through runtime verification (RV) to ensure safety. This stage guarantees the safety of robots’ behaviors before training. In the third stage, the verified SCMG guides SCPO (safety-constrained policy optimization) to obtain an optimized safety policy for robots. Finally, a multi-robot warehouse (RWARE) scenario is used for experimental evaluation. The results show that the policy obtained by our framework is safer than existing frameworks and includes a certain degree of optimization.
Dissertations / Theses on the topic "Multi-Robot systems (MRS)"
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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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PANEBIANCO, LUCA. "Design of multi-agent robotic infrastructures for the exploration of complex and non-structured environments." Doctoral thesis, Università Politecnica delle Marche, 2018. http://hdl.handle.net/11566/253092.
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Lo sviluppo di veicoli autonomi per lo svolgimento di missioni in ambienti o situazioni rischiose è un campo di studi considerabile come fondamentale per l’intera umanità. Queste sono considerate molto complesse da diversi punti di vista: i Robot necessitano di operare in ambienti molto complessi che possono essere lascamente strutturati e molto dinamici, richiedendo apposite strategie per la loro esplorazione. In aggiunta, in questi ambienti potrebbe essere richiesta la collaborazione o coordinazione tra i robot, facendo sorgere la necessità di stabilire vere e proprie società capaci di portare a termine la missione assegnatagli.
In letteratura, per risolvere queste problematiche, viene solitamente citata la teoria dei Sistemi Multi-Agente (MAS), capace di modellare diversi componenti software autonomi (chiamati Agenti) che, insieme, possono svolgere compiti che un'unica entità centralizzata non sarebbe in grado di gestire. Gli agenti si dimostrano flessibili quanto basta per ragionare e pianificare in ambienti dinamici e non strutturati, sfruttando una formalizzazione che consente un alto livello di astrazione delle informazioni. Per ottenere ciò, gli agenti devono essere in grado di sfruttare modelli formali per esprimere e comprendere concetti su chi siano, cosa possano fare, dove si trovino e come possano cooperare.
Questa dissertazione analizza diversi sistemi e tecnologie di intelligenza distribuita attraverso una revisione di un ampio stato dell’arte e introduce il design di un’infrastruttura, basata sulla teoria dei MAS, che consente l’esplorazione di ambienti complessi e non strutturati. L’infrastruttura proposta, che è l’argomento innovativo di questa dissertazione, si pone due obiettivi: definire modelli che riguardano diversi aspetti di robot e Sistemi Multi-Robot e introdurre la struttura di un middleware capace di equipaggiare diverse tipologie di robot, elaborare i modelli proposti e gestire l’intero Sistema Multi-Robot.The development of autonomous vehicles to perform mission in critical and hazardous environments or situations is a field of study that can be considered fundamental for the whole mankind. This task can be considered very complex from different point of view. Robots have to face and operate in complex environments and scenarios that can be loosely structured and mutable, requiring tailored strategies to navigate. In addition, another level of complexity is given from scenario that considers cooperation and/or coordination between robots, where different vehicles need to build a society, exchange and share information to perform a joint mission. In literature, to manage these kind of complexity, Multi-Agent System (MAS) theory is cited as a tool capable to manage this issue, by modelling autonomous software components (called agent) that, together, can perform activities than one unique entity wouldn’t be able to perform, or with worse performances. These agents can be flexible enough to reason and plan in a dynamic and unstructured environment by means of a higher level of abstraction, which can be provided by means of a formalization. Because of this, agents could exploit formal models to express concepts such as what they are, what they can do, where they are and how they can cooperate to perform a given mission. The dissertation analyses different systems and technologies for distributed intelligence through a review of a wide state-of-the-art and introduces the design of an architecture of a multi-robot infrastructure for the exploration of complex, loosely structured environments by means of the MAS theory. The proposed infrastructure, that is the innovative aspect of this dissertation, has two objectives: to express different aspects of Robot and Multi-Robot systems by means of models and to introduce the layout of a middleware that can equip different kind of robots, interpret the proposed models and manage the whole Multi-Robot System.
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Kancir, Pierre. "Méthodologie de conception de système multi-robots : de la simulation à la démonstration." Thesis, Lorient, 2018. http://www.theses.fr/2018LORIS519/document.
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Méthodologie de Conception de Système Multi-robots : de la Simulation à la Démonstration. Les systèmes multi-robots sont des systèmes complexes mais prometteurs dans de nombreux domaines, les nombreux travaux académiques dans ce domaine attestent de l'importance qu'ils auront dans le futur. Cependant, si ces promesses sont réelles, elles ne sont pas encore réalisées comme en témoigne le faible nombre de systèmes multi-robots utilisés dans l'industrie. Pourtant des solutions existent afin de permettre aux industriels et académiques de travailler ensemble à cette problématique. Nous proposons un état de l'art et les défis associés à la conception des systèmes multi-robots d'un point de vue académique et industriel. Nous présentons ensuite trois contributions pour la conception de ces systèmes : une réalisation d'un essaim hétérogène en tant que cas d'étude pratique afin de mettre en évidence les obstacles de conception. La modification d'un autopilote et d'un simulateur pour les rendre compatibles aux développements des systèmes multi-robots. La démonstration d'un outil d'évaluation sur la base des deux contributions précédentes. Enfin, nous concluons sur la portée de ces travaux et des perspectives à venir sur la base de l'open sourceMulti-robot System Design Methodology : from Simulation to Demonstration Multi-robot systems are complex but promising systems in many fields, the number of academic works in this field underlines the importance they will have in the future. However, while these promises are real, they have not yet been realized, as evidenced by the small number of multi-robot systems used in the industry. However, solutions exist to enable industrialists and academics to work together on this issue. We propose a state of the art and challenges associated with the design of multi-robot systems from an academic and industrial point of view. We then present three contributions for the design of these systems: a realization of a heterogeneous swarm as a practical case study in order to highlight the design obstacles. The modification of an autopilot and a simulator to make them compatible with the development of multi-robot systems. Demonstration of an evaluation tool based on the two previous contributions. Finally, we conclude on the scope of this work and future perspectives based on open source
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Engwirda, Anthony, and N/A. "Self-Reliance Guidelines for Large Scale Robot Colonies." Griffith University. Griffith School of Engineering, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20070913.100750.
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A Large Scale Robot Colony (LSRC) is a complex artifact comprising of a significant population of both mobile and static robots. LSRC research is in its literary infancy and it is therefore necessary to rely upon external fields for the appropriate framework, Multi Agent Systems (MAS) and Large Scale Systems (LSS). At the intersection of MAS, LSS and LSRC exist near identical issues, problems and solutions. If attention is paid to coherence then solution portability is possible. The issue of Self-Reliability is poorly addressed by the MAS research field. Disparity between the real world and simulation is another area of concern. Despite these deficiencies, MAS and LSS are perceived as the most appropriate frameworks. MAS research focuses on three prime areas, cognitive science, management and interaction. LSRC is focused on Self-Sustainability, Self-Management and Self-Organization. While LSS research was not primarily intended for populations of mobile robots, it does address key issues of LSRC, such as effective sustainability and management. Implementation of LSRC that is based upon the optimal solution for any one or two of the three aspects will be inferior to a coherent solution based upon all three. LSRCs are complex organizations with significant populations of both static and mobile robots. The increase in population size and the requirement to address the issue of Self-Reliance give rise to new issues. It is no longer sufficient to speak only in terms of robot intelligence, architecture, interaction or team behaviour, even though these are still valid topics. Issues such as population sustainability and management have greater significance within LSRC. As the size of a robot populations increases, minor uneconomical decisions and actions inhibit the performance of the population. Interaction must be made economical within the context of the LSRC. Sustainability of the population becomes significant as it enables stable performance and extended operational lifespan. Management becomes significant as a mechanism to direct the population so as to achieve near optimal performance. The Self-Sustainability, Self-Management and Self-Organization of LSRC are vastly more complex than in team robotics. Performance of the overall population becomes more significant than individual or team achievement. This thesis is a presentation of the Cooperative Autonomous Robot Colony (CARC) architecture. The CARC architecture is novel in that it offers a coherent baseline solution to the issue of mobile robot Self-Reliance. This research uses decomposition as a mechanism to reduce problem complexity. Self-Reliance is decomposed into Self-Sustainability, Self-Management, and Self-Organization. A solution to the issue of Self-Reliance will comprise of conflicting sub-solutions. A product of this research is a set of guidelines that manages the conflict of sub-solutions and maintains a coherent solution. In addressing the issue of Self-Reliance, it became apparent that Economies of Scale, played an important role. The effects of Economies of Scale directed the research towards LSRCs. LSRCs demonstrated improved efficiency and greater capability to achieve the requirements of Self-Reliance. LSRCs implemented with the CARC architecture would extend human capability, enabling large scale operations to be performed in an economical manner, within real world and real time environments, including those of a remote and hostile nature. The theory and architecture are supported using published literature, experiments, observations and mathematical projections. Contributions of this work are focused upon the three pillars of Self-Reliance addressed by CARC: Self-Sustainability, Self-Management and Self-Organization. The chapter on Self-Sustainability explains and justifies the relevance of this issue, what it is, why it is important and how it can be achieved. Self-Sustainability enables robots to continue to operate beyond disabling events by addressing failure and routine maintenance. Mathematical projections are used to compare populations of non-sustained and sustained robots. Computer modeling experiments are used to demonstrate the feasibility of Self-Sustainability, including extended operational life, the maintenance of optimal work flow and graceful physical degradation (GPD). A detailed explanation is presented of Sustainability Functions, Colony Sites, Static Robot Roles, Static Robot Failure Options, and Polymorphism. The chapter on Self-Management explores LSS research as a mechanism to exert influence over a LSRC. An experimental reactive management strategy is demonstrated. This strategy while limited does indicate promising potential directions for future research including the Man in the Loop (MITL) strategy highly desired by NASA JPL for off world command and control of a significant robot colony (Huntsberger, et. al., 2000). Experiments on Communication evaluate both Broadcast Conveyance (BC) and Message Passing Conveyance (MPC). These experiments demonstrate the potential of Message Passing as a low cost system for LSRC communication. Analysis of Metrics indicates that a Performance Based Feedback Method (PBFM) and a Task Achievement Method (TAM) are both necessary and sufficient to monitor a LSRC. The chapter on Self-Organization describes a number of experiments, algorithms and protocols on Reasoning Robotics, a minor variant of Reactive Robotics. Reasoning Robotics utilizes an Event Driven Architecture (EDA) rather than a Stimulus Driven Architecture (SDA) common to Reactive Robotics. Enhanced robot performance is demonstrated by a combination of EDA and environmental modification enabling stigmergy. These experiments cover Intersection Navigation with contingency for Multilane Intersections, a Radio Packet Controller (RPC) algorithm, Active and Passive Beacons including a communication protocol, mobile robot navigation using Migration Decision Functions (MDFs), including MDF positional errors. The central issue addressed by this thesis is the production of Self-Reliance guidelines for LSRCs. Self-Reliance is perceived as a critical issue in advancing the useful and productive applications for LSRCs. LSRCs are complex with many issues in related fields of MAS and LSS. Decomposition of Self-Reliance into Self-Sustainability, Self-Management and Self-Organization were used to aid in problem understanding. It was found that Self-Sustainability extends the operational life of individual robots and the LSRC. Self-Management enables the exertion of human influence over the LSRC, such that the ratio of humans to robots is reduced but not eliminated. Self-Organization achieves and enhances performance through a routine and reliable LSRC environment. The product of this research was the novel CARC architecture, which consists of a set of Self-Reliance guidelines and algorithms. The Self-Reliance guidelines manage conflict between optimal solutions and provide a framework for LSRC design. This research was supported by literature, experiments, observations and mathematical projections.
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Engwirda, Anthony. "Self-Reliance Guidelines for Large Scale Robot Colonies." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/368079.
Full textAbstract:
A Large Scale Robot Colony (LSRC) is a complex artifact comprising of a significant population of both mobile and static robots. LSRC research is in its literary infancy and it is therefore necessary to rely upon external fields for the appropriate framework, Multi Agent Systems (MAS) and Large Scale Systems (LSS). At the intersection of MAS, LSS and LSRC exist near identical issues, problems and solutions. If attention is paid to coherence then solution portability is possible. The issue of Self-Reliability is poorly addressed by the MAS research field. Disparity between the real world and simulation is another area of concern. Despite these deficiencies, MAS and LSS are perceived as the most appropriate frameworks. MAS research focuses on three prime areas, cognitive science, management and interaction. LSRC is focused on Self-Sustainability, Self-Management and Self-Organization. While LSS research was not primarily intended for populations of mobile robots, it does address key issues of LSRC, such as effective sustainability and management. Implementation of LSRC that is based upon the optimal solution for any one or two of the three aspects will be inferior to a coherent solution based upon all three. LSRC’s are complex organizations with significant populations of both static and mobile robots. The increase in population size and the requirement to address the issue of Self-Reliance give rise to new issues. It is no longer sufficient to speak only in terms of robot intelligence, architecture, interaction or team behaviour, even though these are still valid topics. Issues such as population sustainability and management have greater significance within LSRC. As the size of a robot populations increases, minor uneconomical decisions and actions inhibit the performance of the population. Interaction must be made economical within the context of the LSRC. Sustainability of the population becomes significant as it enables stable performance and extended operational lifespan. Management becomes significant as a mechanism to direct the population so as to achieve near optimal performance. The Self-Sustainability, Self-Management and Self-Organization of LSRC are vastly more complex than in team robotics. Performance of the overall population becomes more significant than individual or team achievement. This thesis is a presentation of the Cooperative Autonomous Robot Colony (CARC) architecture. The CARC architecture is novel in that it offers a coherent baseline solution to the issue of mobile robot Self-Reliance. This research uses decomposition as a mechanism to reduce problem complexity. Self-Reliance is decomposed into Self-Sustainability, Self-Management, and Self-Organization. A solution to the issue of Self-Reliance will comprise of conflicting sub-solutions. A product of this research is a set of guidelines that manages the conflict of sub-solutions and maintains a coherent solution. In addressing the issue of Self-Reliance, it became apparent that Economies of Scale, played an important role. The effects of Economies of Scale directed the research towards LSRC’s. LSRC’s demonstrated improved efficiency and greater capability to achieve the requirements of Self-Reliance. LSRC’s implemented with the CARC architecture would extend human capability, enabling large scale operations to be performed in an economical manner, within real world and real time environments, including those of a remote and hostile nature. The theory and architecture are supported using published literature, experiments, observations and mathematical projections. Contributions of this work are focused upon the three pillars of Self-Reliance addressed by CARC: Self-Sustainability, Self-Management and Self-Organization. The chapter on Self-Sustainability explains and justifies the relevance of this issue, what it is, why it is important and how it can be achieved. Self-Sustainability enables robots to continue to operate beyond disabling events by addressing failure and routine maintenance. Mathematical projections are used to compare populations of non-sustained and sustained robots. Computer modeling experiments are used to demonstrate the feasibility of Self-Sustainability, including extended operational life, the maintenance of optimal work flow and graceful physical degradation (GPD). A detailed explanation is presented of Sustainability Functions, Colony Sites, Static Robot Roles, Static Robot Failure Options, and Polymorphism. The chapter on Self-Management explores LSS research as a mechanism to exert influence over a LSRC. An experimental reactive management strategy is demonstrated. This strategy while limited does indicate promising potential directions for future research including the Man in the Loop (MITL) strategy highly desired by NASA JPL for off world command and control of a significant robot colony (Huntsberger, et. al., 2000). Experiments on Communication evaluate both Broadcast Conveyance (BC) and Message Passing Conveyance (MPC). These experiments demonstrate the potential of Message Passing as a low cost system for LSRC communication. Analysis of Metrics indicates that a Performance Based Feedback Method (PBFM) and a Task Achievement Method (TAM) are both necessary and sufficient to monitor a LSRC. The chapter on Self-Organization describes a number of experiments, algorithms and protocols on Reasoning Robotics, a minor variant of Reactive Robotics. Reasoning Robotics utilizes an Event Driven Architecture (EDA) rather than a Stimulus Driven Architecture (SDA) common to Reactive Robotics. Enhanced robot performance is demonstrated by a combination of EDA and environmental modification enabling stigmergy. These experiments cover Intersection Navigation with contingency for Multilane Intersections, a Radio Packet Controller (RPC) algorithm, Active and Passive Beacons including a communication protocol, mobile robot navigation using Migration Decision Functions (MDF’s), including MDF positional errors. The central issue addressed by this thesis is the production of Self-Reliance guidelines for LSRC’s. Self-Reliance is perceived as a critical issue in advancing the useful and productive applications for LSRC’s. LSRC’s are complex with many issues in related fields of MAS and LSS. Decomposition of Self-Reliance into Self-Sustainability, Self-Management and Self-Organization were used to aid in problem understanding. It was found that Self-Sustainability extends the operational life of individual robots and the LSRC. Self-Management enables the exertion of human influence over the LSRC, such that the ratio of humans to robots is reduced but not eliminated. Self-Organization achieves and enhances performance through a routine and reliable LSRC environment. The product of this research was the novel CARC architecture, which consists of a set of Self-Reliance guidelines and algorithms. The Self-Reliance guidelines manage conflict between optimal solutions and provide a framework for LSRC design. This research was supported by literature, experiments, observations and mathematical projections.Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Griffith School of Engineering
Faculty of Engineering and Information Technology
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(10725849), Minji Lee. "INTELLIGENT SELF ADAPTING APPAREL TO ADAPT COMFORT UTILITY." Thesis, 2021.
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Enhancing the capability to control a tremendous range of physical actuators and sensors, combined with wireless technology and the Internet of Things (IoT), apparel technologies play a significant role in supporting safe, comfortable and healthy living, observing each customer’s conditions. Since apparel technologies have advanced to enable humans to work as a team with the clothing they wear, the interaction between a human and apparel is further enhanced with the introduction of sensors, wireless network, and artificially intelligent techniques. A variety of wearable technologies have been developed and spread to meet the needs of customers, however, some wearable devices are considered as non-practical tech-oriented, not consumer-oriented.
The purpose of this research is to develop an apparel system which integrates intelligent autonomous agents, human-based sensors, wireless network protocol, mobile application management system and a zipper robot. This research is an augmentation to the existing research and literature, which are limited to the zipping and unzipping process without much built in intelligence. This research is to face the challenges of the elderly and people with self-care difficulties. The intent is to provide a scientific path for intelligent zipper robot systems with potential, not only to help people, but also to be commercialized.
The research develops an intelligent system to control of zippers fixed on garments, based on the profile and desire of the human. The theoretical and practical elements of developing small, integrated, intelligent zipper robots that interact with an application by using a lightweight MQTT protocol for application in the daily lives of diverse populations of people with physical challenges. The system functions as intelligent automatized garment to ensure users could positively utilize a zipper robot device to assist in putting on garments which also makes them feel comfortable wearing and interacting with the system. This research is an approach towards the “future of fashion”, and the goal is to incentivize and inspire others to develop new instances of wearable robots and sensors that help people with specific needs to live a better life.
Books on the topic "Multi-Robot systems (MRS)"
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Staff, IEEE. 2021 International Symposium on Multi Robot and Multi Agent Systems (MRS). IEEE, 2021.
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2023 International Symposium on Multi Robot and Multi Agent Systems (MRS). IEEE, 2023.
Find full textBook chapters on the topic "Multi-Robot systems (MRS)"
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Skarzynski, Kamil, Marcin Stepniak, Waldemar Bartyna, and Stanislaw Ambroszkiewicz. "SO-MRS: A Multi-robot System Architecture Based on the SOA Paradigm and Ontology." In Towards Autonomous Robotic Systems, 330–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96728-8_28.
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Chand, Roneel, Krishna Raghuwaiya, Jito Vanualailai, and Jai Raj. "Leader-Follower Based Control of Fixed-Wing Multi-Robot System (MRS) via Split-Rejoin Maneuvers in 3D." In Lecture Notes in Networks and Systems, 195–209. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9228-5_18.
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M., Fabio. "Spatiotemporal MCA Approach for the Motion Coordination of Heterogeneous MRS." In Recent Advances in Multi Robot Systems. I-Tech Education and Publishing, 2008. http://dx.doi.org/10.5772/5480.
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Marchese, Fabio. "Time-Invariant Motion Planner in Discretized C-Spacetime for MRS." In Multi-Robot Systems, Trends and Development. InTech, 2011. http://dx.doi.org/10.5772/13388.
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Ross, Gennady, and Valery Konyavsky. "The Methods for Emergency Robot Self-Оrganization Control." In Advances in Digital Science - ADS 2022, 5–25. Institute of Certified Specialists (ICS), 2022. http://dx.doi.org/10.33847/978-5-6048575-0-2_1.
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The chapter demonstrates that the efficiency of self-organization of multi-agent systems (MAS) for controlling robots in emergency situations can be significantly increased if robots are provided with the ability to plan their behavior. The principles, requirements and tasks of the robot self-organization control are described. A concept for the development of evolutionary simulation methods (market models), which include a set of interlinked simulation models for swarm robot self-organization control, is proposed. The example of an algorithm for MAS robotic emergency control is given.
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Tian, Yongkai, Xin Yu, Yirong Qi, Li Wang, Pu Feng, Wenjun Wu, Rongye Shi, and Jie Luo. "Exploiting Hierarchical Symmetry in Multi-Agent Reinforcement Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240741.
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Achieving high sample efficiency is a critical research area in reinforcement learning. This becomes extremely difficult in multi-agent reinforcement learning (MARL), as the capacity of the joint state and action space grows exponentially with the number of agents. The reliance of MARL solely on exploration and trial-and-error, without incorporating prior knowledge, exacerbates the issue of low sample efficiency. Currently, introducing symmetry into MARL is an effective approach to address this issue. Yet the concept of hierarchical symmetry, which maintains symmetry across different levels of a multi-agent system (MAS), has not been explored in existing methods. This paper focuses on multi-agent cooperative tasks and proposes a method incorporating hierarchical symmetry, termed the Hierarchical Equivariant Policy Network (HEPN) which is O(n)-equivariant. Specifically, HEPN utilizes clustering to perform hierarchical information extraction in MAS, and employs graph neural networks to model agent interactions. We conducted extensive experiments across various multi-agent tasks. The results indicate that our method achieves faster convergence speeds and higher convergence rewards compared to baseline algorithms. Additionally, we have deployed our algorithm in a physical multi-robot system, confirming its effectiveness in real-world environments. Supplementary materials are available at https://yongkai-tian.github.io/HEPN/.
Conference papers on the topic "Multi-Robot systems (MRS)"
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Renganathan, Venkatraman, and Tyler Summers. "Spoof resilient coordination for distributed multi-robot systems." In 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2017. http://dx.doi.org/10.1109/mrs.2017.8250942.
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Wu, Fang, Vivek Shankar Varadharajan, and Giovanni Beltrame. "Collision-aware Task Assignment for Multi-Robot Systems." In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2019. http://dx.doi.org/10.1109/mrs.2019.8901059.
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Capelli, Beatrice, Cristian Secchi, and Lorenzo Sabattini. "Communication Through Motion: Legibility of Multi-Robot Systems." In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2019. http://dx.doi.org/10.1109/mrs.2019.8901100.
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Villani, Valeria, Lorenzo Sabattini, Cristian Secchi, and Cesare Fantuzzi. "Natural interaction based on affective robotics for multi-robot systems." In 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2017. http://dx.doi.org/10.1109/mrs.2017.8250931.
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Dixit, Gaurav, Nicholas Zerbel, and Kagan Tumer. "Dirichlet-Multinomial Counterfactual Rewards for Heterogeneous Multiagent Systems." In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2019. http://dx.doi.org/10.1109/mrs.2019.8901077.
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Dobson, Andrew, Kiril Solovey, Rahul Shome, Dan Halperin, and Kostas E. Bekris. "Scalable asymptotically-optimal multi-robot motion planning." In 2017 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2017. http://dx.doi.org/10.1109/mrs.2017.8250940.
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Korngut, Yair, and Noa Agmon. "Multi-Robot Heterogeneous Adversarial Coverage." In 2023 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2023. http://dx.doi.org/10.1109/mrs60187.2023.10416778.
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Mitrano, Peter, Jordan Burklund, Michael Giancola, and Carlo Pinciroli. "A Minimalistic Approach to Segregation in Robot Swarms." In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2019. http://dx.doi.org/10.1109/mrs.2019.8901068.
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Yadav, Indrajeet, and Herbert G. Tanner. "Mobile Radiation Source Interception by Aerial Robot Swarms." In 2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2019. http://dx.doi.org/10.1109/mrs.2019.8901102.
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De Carli, Nicola, Paolo Salaris, and Paolo Robuffo Giordano. "Online Decentralized Perception-Aware Path Planning for Multi-Robot Systems." In 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS). IEEE, 2021. http://dx.doi.org/10.1109/mrs50823.2021.9620694.
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