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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 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.

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.

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.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

The purpose of this paper is to present research in the area of the applications of knowledge‐based and constraint programming (CP)‐driven methodology in production planning and development of decision‐making software supporting scheduling of multi‐robot in a multi‐product job shop, taking into account imprecise (fuzzy) activity specification, and resource sharing by some industrial processes that simultaneously produce different products.

Applications of the knowledge‐based, logic‐algebraic and CP‐driven approach for multi‐robot task allocation problem and generating of fuzzy plan/schedule of production activities for a given period of time.

This paper illustrates the useful information that can be obtained from fuzzy and crispy‐like schedule describing production activities in a multi‐product job shop.

The use of knowledge‐based and CP‐driven methodology for production planning in a multi‐product job shop was a very effective method dedicated to solve typical decision problems in the area of project‐driven production flow management applied in make‐to‐order manufacturing.

The methodology discussed in the paper can be used to design fuzzy Gantt diagrams, which define admissible schedule of production orders for a given period of time.

The paper's contribution covers various issues of decision making while employing the knowledge‐ and CP‐based framework. The proposed approach provides the framework allowing one to take into account distinct (pointed), and imprecise (fuzzy) data, in a unified way and treat it in a unified form of a discrete, constraint satisfaction problem.

With the rapid development of e-commerce, logistics demand is increasing day by day. The modern warehousing with a multi-agent system as the core comes into being. This paper aims to study the task allocation and path-planning (TAPP) problem as required by the multi-agent warehouse system.

The TAPP problem targets to minimize the makespan by allocating tasks to the agents and planning collision-free paths for the agents. This paper presents the Hierarchical Genetic Highways Algorithm (HGHA), a hierarchical algorithm combining optimization and multi-agent path-finding (MAPF). The top-level is the genetic algorithm (GA), allocating tasks to agents in an optimized way. The lower level is the so-called highways local repair (HLR) process, avoiding the collisions by local repairment if and only if conflicts arise.

Experiments demonstrate that HGHA performs faster and more efficient for the warehouse scenario than max multi-flow. This paper also applies HGHA to TAPP instances with a hundred agents and a thousand storage locations in a customized warehouse simulation platform with MultiBots.

This paper formulates the multi-agent warehousing distribution problem, TAPP. The HGHA based on hierarchical architecture solves the TAPP accurately and quickly. Verifying the HGHA by the large-scale multi-agent simulation platform MultiBots.

The purpose of this paper is to present a novel automatic planning and coordinated control method of redundant dual‐arm space robot for inner space‐station operation based on multiple sensors information by stages.

In order to improve the coordinated control capability of dual‐arm robot system, a four‐layer hierarchical control structure is designed based on the theory of centralization and decentralization. At the high‐level planning of dual‐arm system, a task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning. Finally, a 3D simulation and experiment of screwing nut and bolt are implemented on a dual‐arm robot system, and the feasibility and applicability of this control strategy are verified.

The automatic planning can be accomplished by means of sensors information by stages, and by this method, the autonomy and intelligence of dual‐arm space robot system can be further improved.

A new automatic planning strategy integrated with multiple sensors information by stages is proposed, and can be implemented on a dual‐arm robot system for inner space‐station operations. This method specializes in heterogeneous dual‐arm robot system.

A task decomposition strategy based on task knowledge and a task allocation strategy in terms of the robotic capability are proposed, respectively. Moreover, a control method by stages based on the information of multiple sensors is introduced to object recognition, task planning, path planning and trajectory planning of dual‐arm robot system.

The purpose of this paper is to show how a swarm of robots can cooperate to achieve a common task, in a totally distributed and autonomous way, by exploiting powerful clues contained in some devices that are distributed in the environment. This system exploits a coordination mechanism that is twofold, using radio frequency identification (RFID) tags for spatial coordination, and wireless robot‐to‐robot communication for the temporal and semantic synchronization.

Progress in the pervasive computing field has led to the distribution of knowledge and computational power in the environment, rather than condensing it in a single, powerful entity. This vision of ambient intelligence is supported by the interchange of information between physically sparse agents cooperating to achieve a common goal. An emerging method for this kind of collaboration considers the agents as insects in a swarm, having the possibility of communicating directly or indirectly with each other. The goal is to fulfill a common task, showing that a collaborative behavior can be useful in the real world. The paper focuses on a technique for the coordination of swarm‐robots with low capabilities, driven by instructions learned from RFID tags used as distributed pervasive memories. These robots exploit ubiquitous computing to regroup in a synchronization area, make a formation in space, coordinate with team‐mates in the same zone, and finally complete a cooperative task. The algorithm is validated through a simulation environment, showing its applicability and performance, before the real implementation on Roomba‐like robots.

The goal of the research is to prove the feasibility of such a novel approach. It is observed that a swarm of robots can achieve a good degree of autonomous cooperation without a central infrastructure or global network, carrying out a goal in a fair time.

The value is given by the benefits of splitting the synchronization semantics into two levels: space, by exploiting RFID landmarks; and time, by exploiting wireless short‐range communication. RFID tags are used to distribute computational power and actively interact with the surrounding areas, allowing to learn and modify the state of the environment. Robot‐to‐robot communication, instead, is used for providing timing and semantic information. In the proposal, this augmented environment is used to allow a good level of coordination among robots, both in time and space, with the aim of building a cooperative system.

The purpose of this paper is to design intelligent robots operating in such dynamic environments like the RoboCup Middle-Size League (MSL). In the RoboCup MSL, two teams of five autonomous robots play on an 18- × 12-m field. Equipped with sensors and on-board computers, each robot should be able to perceive the environment, make decision and control itself to play the soccer game autonomously.

This paper presents the design of our soccer robots, participating in RoboCup MSL. The mechanical platform, electrical architecture and software framework are discussed separately. The mechanical platform is designed modularly, so easy maintainability is achieved; the electronic architecture is built on industrial standards using PC-based control technique, which results in high robustness and reliability during the intensive and fierce MSL games; the software is developed upon the open-source Robot Operating System (ROS); thus, the advantages of ROS such as modularity, portability and expansibility are inherited.

Based on this paper and the open-source hardware and software, the MSL robots can be re-developed easily to participate in the RoboCup MSL. The robots can also be used in other research and education fields, especially for multi-robot systems and distributed artificial intelligence. Furthermore, the main designing ideas proposed in the paper, i.e. using a modular mechanical structure, an industrial electronic system and ROS-based software, provide a common solution for designing general intelligent robots.

The methodology of the intelligent robot design for highly competitive and dynamic RoboCup MSL environments is proposed.