Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

Lozenguez, Guillaume; Adouane, Lounis; Beynier, Aurélie; Mouaddib, Abdel‐Illah; Martinet, Philippe

doi:10.1007/s10514-015-9483-7

Cited by 21 publications

(24 citation statements)

References 31 publications

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“…From the auction to achieve the way, a centralized auction can achieve the optimal task allocation, where the decentralized auction not only makes each robot take a small amount of computation but also has robustness [8]. Guillaume, Lounis, Aurelie, Abdel-Illah and Philippe [11] proposed control architecture based on topological representation of the environment and a protocol based on sequential simultaneous auctions (SSA) which reduces the planning complexity to coordinate Robots' policies. The policies are individually computed using Markov Decision Processes oriented by several goal task positions to reach.…”

Section: Task Allocationmentioning

confidence: 99%

“…The typical method is based on the MDP model in decision-making theory to research the coordination of multi-robot systems. MDP is an effective tool [25,[26][27][28] to deal with uncertainties in robot systems.…”

Section: Dealing With Uncertaintymentioning

confidence: 99%

“…Moreover, the complexity of the auction and decentralized data fusion depends only on the size of the local communication neighborhood of each UAV. Lozenguez, Adouane, Beynier, Mouaddib and Martinet [27] adopted a topological map representation instead of the grid method, which would lead to an explosive growth of the state space of the goal-oriented Markov decision process for each robot. Kim, Baik and Lee [30] studied the task allocation problem of a heterogeneous UAV team performing search and destruction tasks in a dynamic and uncertain environment.…”

Section: Dealing With Uncertaintymentioning

confidence: 99%

“…The scheme is superior to the real-time task allocation method. In the above work, the choice of the action strategy of the decision model is completed by the auction mechanism [25,27,29], the entropy is used as the measure of the uncertainty of the robot [25,26], but the stratified social entropy [31] is not used in these literatures.…”

Section: Dealing With Uncertaintymentioning

confidence: 99%

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Research status of multi - robot systems task allocation and uncertainty treatment

Fan

Dai

2017

J. Phys.: Conf. Ser.

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Abstract. The multi-robot coordination algorithm has become a hot research topic in the field of robotics in recent years. It has a wide range of applications and good application prospects. This paper analyzes and summarizes the current research status of multi-robot coordination algorithms at home and abroad. From task allocation and dealing with uncertainty, this paper discusses the multi-robot coordination algorithm and presents the advantages and disadvantages of each method commonly used. IntroductionMulti-robot systems have become a hot topic of research in the field of robotics. In addition, because of their high fault tolerance and robustness, they are widely used. The multi-robot system still faces problems such as task allocation and resource competition. They are referred to as coordination of teammate robots. Complex problems are divided into a set of sub-problems. The optimization problem is solved by competing and cooperation among robots. The operational capacity is improved. In the study of multi-robot system coordination algorithm, task allocation is an important guarantee for multirobot to complete the task, and it is an important part of task planning. In the multi-robot system, there are many problems such as communication delay, dynamic environment change, sensor precision limit and system component failure. These uncertainties will affect the assignment of multi-robot system tasks, so the research on solving uncertainties is increasingly affected Attention. Therefore, this paper reviews the research of multi-robot systems in the process of exploration and summarizes the latest research trends of multi-robots in cooperation, task allocation and dealing with uncertainty. Task AllocationThe goal of task allocation across a multi-robot system is to maximize the benefits of task execution and to minimize the cost for the entire system. The design goals for task allocation methods are real-time, robustness, reliability, optimization performance, communication requirements, computing requirements, and scale capabilities. There are two ways to distribute the tasks. The first is the way is emergent allocation and the second is intentional cooperative distribution. Emergent allocation is realized coordination by the interaction between each other and through impacts with the environment, which is more suitable for the tasks of large scale multi-robot systems. However, this method cannot predict the behavior of a robot accurately in the system and cannot guarantee the efficiency. Intentional cooperative distribution is achieved through robot display cooperation, easy-to-implement humancomputer interaction, and has the potential to better utilize the capabilities of heterogeneous robotic teams. Intentional cooperative distribution can be divided into centralized distribution and distributed distribution. The centralized allocation has the characteristics of simple structure, strong calculation performance and easy implementation. But it is easy to cause the problem of communication congestion, and dif...

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Section: Task Allocationmentioning

confidence: 99%

Section: Dealing With Uncertaintymentioning

confidence: 99%

Section: Dealing With Uncertaintymentioning

confidence: 99%

Section: Dealing With Uncertaintymentioning

confidence: 99%

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Research status of multi - robot systems task allocation and uncertainty treatment

Fan

Dai

2017

J. Phys.: Conf. Ser.

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“…Communication protocols allow the agents to adapt their policies to reach a common interest. Distributed MDP is used for example in robotic missions to deal with traveling salesmen coordination (Lozenguez et al, 2015).…”

Section: Distributed Mdpmentioning

confidence: 99%

Planning large systems with MDPs: case study of inland waterways supervision

Desquesnes

Lozenguez

Doniec

et al. 2016

ADCAIJ

Self Cite

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KEYWORD ABSTRACTMarkov decision processes; Inland waterway network; Large-scale systemsInland waterway management is likely to go through heavy changes due to an expected traffic increase in a context of climate change. Those changes will require an adaptive and resilient management of the water resource. The aim is to have an optimal plan for the distribution of the water resource on the whole inland waterway network, while taking into account the uncertainties arising from the operations of such a network. A representative model using Markov decision processes is proposed to model the dynamic and the uncertainties of the waterways. The proposed model is able to coordinate multiple entities over multiple time steps in order to prevent an overflow of a test network. However, this model suffers from a lack of scalability and is unable to represent real case applications. Advantages and limitations of several approaches of the literature to circumvent this limitation are discussed according to our case study.

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Predicate logic reasoning for exploration coordination of multi‐robot systems in structured environments

Dai

Wang

Zhao

et al. 2020

Concurrency and Computation

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To improve time efficiency for structured environment exploration of multi-robot systems, a novel coordinated strategy based on predicate logic reasoning is proposed in this paper. The strategy assigns the room with maximal utility to the robot with the optimal exploring property, and realizes cooperative exploration by considering the room capacity. A set of predicate logic reasoning formulas is established to implement the coordination. The coordination takes into account robot performance and environment property and is conducted in a distributed manner. In addition, to save computing and storage resources, a triple set named as task information was proposed instead of traditional unfinished job list for environment information sharing among robots. The results are validated by computer simulations for two simulated office like environments.

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Punctual versus continuous auction coordination for multi-robot and multi-task topological navigation

Cited by 21 publications

References 31 publications

Research status of multi - robot systems task allocation and uncertainty treatment

Research status of multi - robot systems task allocation and uncertainty treatment

Planning large systems with MDPs: case study of inland waterways supervision

Predicate logic reasoning for exploration coordination of multi‐robot systems in structured environments

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