Multi-robot task allocation through vacancy chain scheduling

Dahl, Torbjørn S.; Matarić, Maja J.; Sukhatme, Gaurav S.

doi:10.1016/j.robot.2008.12.001

Cited by 87 publications

(50 citation statements)

References 13 publications

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“…Mataric et al 2003;Lerman et al 2006;Jevtic et al 2012, or the "job completion" problem on a manufacturing floor (e.g. Gerkey and Mataric 2003;Dahl et al 2009;Sarker and Dahl 2011). -Collection worksites represent resource deposits that need to be exploited as soon as possible.…”

Section: Simulation Environment and Swarm Missionsmentioning

confidence: 99%

The Information-Cost-Reward framework for understanding robot swarm foraging

2017

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Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the InformationCost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the informationcentred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design.

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Section: Simulation Environment and Swarm Missionsmentioning

confidence: 99%

The Information-Cost-Reward framework for understanding robot swarm foraging

2017

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“…Our aim is to mimic a situation when a team needs to display a certain composition of agents in order to achieve top performance [57]. For example, multiple robots may be more efficient if they compose distinct groups, each focusing on a different task [58]. In biology, this process is known as division of labor (see [59], [60] and [61] for comprehensive reviews).…”

Section: B Problem Formulationmentioning

confidence: 99%

Evolving Team Compositions by Agent Swapping

Lichocki

Wischmann

Keller

et al. 2013

IEEE Trans. Evol. Computat.

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Abstract-Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have been shown to be a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far it has never been thoroughly quantified. Here we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping that exchanges only corresponding agents between teams and free agent swapping that allows an arbitrary exchange of agents. Our results show that restricted agent swapping suffers from premature convergence, whereas free agent swapping entails insufficient convergence. Consequently, in both cases the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem we propose to combine the two methods. Our approach first applies free agent swapping to explore the search space and then restricted agent swapping to exploit it. This mixed approach turns out to be a much more efficient strategy for the evolution of team compositions compared to either strategy alone. Our results suggest that such a mixed agent swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown.

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“…Hence, care must be taken to minimize disruption to the schedule of other robots when the fractured subteam carries out replanning [27,28]. On the one hand, it is sometimes worth trying to maintain connectivity while performing the assigned task [29][30][31] or using techniques with no direct inter-robot communication [32].…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Coordination in Complex Environment with Task and Communication Constraints

Liu

Yang

Zheng

et al. 2013

International Journal of Advanced Robotic Systems

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The tasks would fail to be assigned to any robots in the task allocation phase as a consequence of the inherent communication constraints in multi-robot systems (MRS). This negative effect becomes even more serious in tasks with temporal constraints. We therefore propose the constraint-based approach (CoBA), a marketbased task allocation approach to enable multi-robot coordination in domains with temporal constraints between subtasks of a complex task and network constraints between robots. We handle network constraints by having each robot maintain a dynamic acquaintance network of robots that it knows about, and allowing a robot to submit a bid on behalf of another robot during a task auction ("indirect bidding"). In order to model the complex task, we introduce the AND/OR task tree with temporal constraints. An auction-clearing routine, which supports the AND/OR task tree with temporal constraints and direct/indirect task auction, is proposed to enable effective multi-robot task allocation in spite of various constraints. The solution was validated in both simulation and physical environments by a series of experiments in disaster response domains. Specifically, we study the system performance by separately varying the number of robots, the expected rate of task issuance, the communication reliability factor, the compositions of MRS, as well as the acquaintance relationship parameter, in simulation experiments. The results suggest that our solution outperforms others, that is, robots were able to complete the tasks more promptly and effectively.

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Multi-robot task allocation through vacancy chain scheduling

Cited by 87 publications

References 13 publications

The Information-Cost-Reward framework for understanding robot swarm foraging

The Information-Cost-Reward framework for understanding robot swarm foraging

Evolving Team Compositions by Agent Swapping

Multi-Robot Coordination in Complex Environment with Task and Communication Constraints

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