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The purpose of this paper is to present a novel approach to coordination of multi‐agent teams, and in particular multi‐robot teams. The new approach is based on models of organisational sociology, namely the concept of social networks. The social relationships used in the model that is presented in this paper are trust and kinship relationships, but modified for use in heterogeneous multi‐robot teams.

The coordination of a robot team is achieved through task allocation. The proposed task allocation mechanism was tested in the multi‐robot team task allocation simulation.

The social networks‐based task allocation algorithm has performed according to expectations and the obtained results are very promising. Some intriguing similarities with higher mammalian societies were observed and they are discussed in this paper. The social networks‐based approach also exhibited the ability to learn and store information using social networks.

The research focused on simulated environments and further research is envisaged in the physical environments to confirm the applicability of the presented approach.

In this paper, the proposed coordination was applied to simulated multi‐robot teams. It is important to note that the proposed coordination model is not robot specific, but can also be applied to almost any multi‐agent system without major modifications.

The paper emphasizes applicability of considering multi‐robot teams as socially embodied agents. It also presents a novel and efficient approach to task allocation.

The Group of Unmanned Assistant Robots Deployed in Aggregative Navigation by Scent (GUARDIANS) multi‐robot team is to be deployed in a large warehouse in smoke. The team is to assist firefighters search the warehouse in the event or danger of a fire. The large dimensions of the environment together with development of smoke which drastically reduces visibility, represent major challenges for search and rescue operations. The GUARDIANS robots act alongside a firefighter and guide and accompany the firefighters on the site while indicating possible obstacles and the locations of danger and maintain communications links. The purpose of this paper is to focus on basic navigation behaviours of multi‐robot or human‐robot teams, which have to be achieved without central and on‐line control in both categories of GUARDIANS robots' tasks.

In order to fulfill the aforementioned tasks, the robots need to be able to perform certain behaviours. Among the basic behaviours are capabilities to stay together as a group, that is, generate a formation and navigate while keeping this formation. The control model used to generate these behaviours is based on the so‐called social potential field framework, which the authors adapt to the specific tasks required for the GUARDIANS scenario. All tasks can be achieved without central control, and some of the behaviours can be performed without explicit communication between the robots.

The GUARDIANS environment requires flexible formations of the robot team: the formation has to adapt itself to the circumstances. Thus, the application has forced the concept of a formation to be re‐defined. Using the graph‐theoretic terminology, it can be said that a formation may be stretched out as a path or be compact as a star or wheel. The developed behaviours have been implemented in simulation environments as well as on real ERA‐MOBI robots commonly referred to as Erratics. Advantages and shortcomings of the model, based on the simulations as well as on the implementation with a team of Erratics are discussed.

This paper discusses the concept of a robot formation in the context of a real world application of a robot team (Swarm).

This paper aims to develop a service‐oriented distributed multi‐robot system based on manufacturing message specification (MMS) and new‐generation distributed object technology – web services for realizing remotely monitoring and controlling multiple heterogeneous robots in the internet environment.

The study presents robot communication model and distributed multi‐robot monitoring and control software structure based on MMS and web services. In particular, monitoring and control software design of MMS concepts in web services environment using Unified Modeling Language model is discussed in detail. In addition, to verify the validity of the proposed design method, a multi‐robot prototype system for robot flexible assemble cell has been achieved. Its Server software is implemented in C++ with Visual Studio.NET being the development environment and Client software is programmed in Java with Borland JBuilder 9 being the development tool.

Finds that the communication structure following MMS can make the multi‐robot monitoring and control system have perfect robustness, interoperability and reconfigurability. Besides, web services technology can conveniently realize MMS services, also can successfully resolve the remote multi‐robot monitoring and control problem among cross‐network, cross‐platform and heterogeneous systems.

Provides an easy and low‐cost method for realizing heterogeneous multi‐robot remote driving. The web‐based distribution of the presented system is critical in enabling capabilities such as e‐manufacturing, e‐diagnostics and e‐maintenance.

The proposed system can be seamlessly integrated into other automated manufacturing systems or management systems in plug‐and‐play fashion. The combination of MMS and web services is in favor of real manufacturing equipments being embedded in the network, so the presented systematic methodology can be a useful reference for constructing web‐based reconfigurable manufacturing systems.

Provides robot communication model based on MMS and web services and presents service‐oriented distributed remote multi‐robot monitoring and control software architecture.

Multi-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.

This paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.

Allowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.

To the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods.

This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems.

This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies.

This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

The multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD³GPSO.

This approach is made out of two phases: phase I groups the tasks into clusters using the ACD³GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD³GPSO for better results. First, ACD³GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid.

Computational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time.

The proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks.

In this methodology, owing to the ACD³GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

The purpose of this paper is to describe a multi‐robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short‐range, low‐power sensing, communication, and processing capabilities trace a chemical plume to its source emitter

The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution.

The new empirical simulations confirmed previous theoretical predictions: a physics‐based approach is more effective than the biologically inspired methods in meeting the objectives of the plume‐tracing mission. This gain in performance is consistent across a variety of plume and environmental conditions. This work shows that high success rate can be achieved by robots using strictly local information and a fully decentralized, fault‐tolerant, and reactive control algorithm.

This is the first paper to compare a physics‐based approach against the leading alternatives for chemical plume tracing under a wide variety of fluid conditions and performance metrics. This is also the first presentation of the algorithms showing the specific mechanisms employed to achieve superior performance, including the underlying fluid and other physics principles and their numerical implementation, and the mechanisms that allow the practitioner to duplicate the outstanding performance of this approach under conditions of many robots navigating through obstacle‐dense environments.

The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search. Moreover, users who issue the query may not learn of this inconsistency even after a data cleaning process (e.g. schema matching or data screening). The inconsistency can exist in different types of data, such as temporal or spatial data. Therefore, this paper aims to introduce an information fusion method that can detect data inconsistency in the early stages of data fusion.

This paper introduces an information fusion method for multi-robot operations, for which fusion is conducted continuously. When the environment is explored by multiple robots, the robot logs can provide more information about the number and coordination of targets or victims. The information fusion method proposed in this paper generates an underdetermined linear system of overlapping spatial reports and estimates the case values. Then, the least squares method is used for the underdetermined linear system. By using these two methods, the conflicts between reports can be detected and the values of the intervals at specific times or locations can be estimated.

The proposed information fusion method was tested for inconsistency detection and target projection of spatial fusion in sensor networks. The proposed approach examined the values of sensor data from simulation that robots perform search tasks. This system can be expanded to data warehouses with heterogeneous data sources to achieve completeness, robustness and conciseness.

Little research has been devoted to the linear systems for information fusion of tasks of mobile robots. The proposed information fusion method minimizes the cost of time and comparison for data fusion and also minimizes the probability of errors from incorrect results.

The purpose of this paper is to address a classic problem – pattern formation identified by researchers in the area of swarm robotic systems – and is also motivated by the need for mathematical foundations in swarm systems.

The work is separated out as inspirations, applications, definitions, challenges and classifications of pattern formation in swarm systems based on recent literature. Further, the work proposes a mathematical model for swarm pattern formation and transformation.

A swarm pattern formation model based on mathematical foundations and macroscopic primitives is proposed. A formal definition for swarm pattern transformation and four special cases of transformation are introduced. Two general methods for transforming patterns are investigated and a comparison of the two methods is presented. The validity of the proposed models, and the feasibility of the methods investigated are confirmed on the Traer Physics and Processing environment.

This paper helps in understanding the limitations of existing research in pattern formation and the lack of mathematical foundations for swarm systems. The mathematical model and transformation methods introduce two key concepts, namely macroscopic primitives and a mathematical model. The exercise of implementing the proposed models on physics simulator is novel.

In a soccer robot game, the environment is highly competitive and dynamic. In order to work in the dynamically changing environment, the decision‐making system of a soccer robot system should have the features of flexibility and real‐time adaptation. The purpose of this paper is to focus on the middle‐size soccer robot league (MSL) and present new hierarchical hybrid fuzzy methods for decision making and action selection of an MSL robot.

In this paper, new hierarchical hybrid fuzzy methods for decision making and action selection of a robot in MSL are presented. First, the behaviors of an agent are introduced, implemented and classified in two layers, the low‐level behaviors and the high‐level behaviors. In the second layer, a two‐phase mechanism for decision making is introduced. In phase one, some useful methods are implemented which check the robot's situation for performing required behaviors. In the next phase, the team strategy, team formation, robot's role and the robot's positioning system are introduced. A fuzzy logical approach is employed to recognize the team strategy and furthermore to tell the player the best position to move.

This methodology was implemented on the ADRO RoboCup Team and ADRO team performance 2008 was compared with its previous version 2007. The results showed the success of this methodology; the team performance in coordination and collaboration highly improved; in fact, the players switched their strategic area smoothly as the team strategy changed in a reasonable manner, the robots carried out the high‐level behaviors much more efficiently and the final results were enhanced significantly.

This paper is a result of the authors' original research work in the field of autonomous robot‐middle size soccer robot, supported by Islamic Azad University – Khorasgan Branch, Isfahan, Iran.