A Machine Learning Method for Improving Task Allocation in Distributed Multi-Robot Transportation

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

doi:10.1007/3-540-32834-3_14

Cited by 18 publications

(13 citation statements)

References 11 publications

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“…In the multi-robot domain, existing methods for the solution of these combinatorial optimization problems often reduce to market-based approaches (Lin and Zheng 2005;Guerrero and Oliver 2003;Jones et al 2006) where robots must execute complex bidding schemes to determine the appropriate allocation based on the various perceived costs and utilities. While market-based approaches have gained much success in various multi-robot applications Vail and Veloso 2003;Gerkey and Mataric 2002;Jones et al 2007) and can be further improved when learning is incorporated (Dahl et al 2006), these methods often scale poorly in terms of team size and number of tasks (Dias 2004;Golfarelli and Maio 1997).…”

mentioning

confidence: 99%

Biologically inspired redistribution of a swarm of robots among multiple sites

et al. 2008

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We present a biologically inspired approach to the dynamic assignment and reassignment of a homogeneous swarm of robots to multiple locations, which is relevant to applications like search and rescue, environmental monitoring, and task allocation. Our work is inspired by experimental studies of ant house hunting and empirical models that predict the behavior of the colony that is faced with a choice between multiple candidate nests. We design quorum based stochastic control policies that enable the team of agents to distribute themselves among multiple candidate sites in a specified ratio, and compare our results to the linear stochastic policies described in (Halasz et al.We show how our quorum model consistently performs better than the linear models while minimizing computational requirements and now it can be implemented without the use of inter-agent wireless communication.

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confidence: 99%

Biologically inspired redistribution of a swarm of robots among multiple sites

et al. 2008

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“…Topologically independent algorithms have been developed that apply both locally and globally [48] and PSO has interestingly been utilized in mixed signal analysis [37]. Swarm intelligence has also been applied to teams of robots [41], and several successful applications specific to multi-robot coordination include search and rescue [8,26,49], robotic soccer [19], mobile sensor networks [16,30], mine collection [11], and patrol with adversaries [2].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Partnership Formation for Multi-Rover Coordination

Knudson

Tumer

2013

Advs. Complex Syst.

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Coordinating multiagent systems to maximize global information collection is a key challenge in many real world applications such as planetary exploration, and search and rescue. In particular, in many domains where communication is expensive (e.g., in terms of energy), the coordination must be achieved in a passive manner, without agents explicitly informing other agents of their states and/or intended actions. In this work, we extend results on such multiagent coordination algorithms to domains where the agents cannot achieve the required tasks without forming teams. We investigate team formation in three types of domains, one where n agents need to perform a task for the team to receive credit, one where there is an optimal number of agents (n) required for the task, but where the agents receive a decaying reward if they form a team with membership other than n, and finally we investigate heterogeneous teams where individuals vary in construction. Our results show that encouraging agents to coordinate is more successful than strictly requiring coordination. We also show that devising agent objective functions that are aligned with the global objective and locally computable significantly outperform systems where agents directly use the global objective, and that the improvement increases with the complexity of the task.

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“…While this problem can be formulated as a multi-task (MT), single-robot (SR), time-extended assignment (TA) problem (Gerkey and Mataric, 2004), existing approaches do not take into account the effects of fluid dynamics coupled with the inherent environmental noise (Gerkey and Mataric, 2002;Dias et al, 2006;Dahl et al, 2006;Hsieh et al, 2008;Berman et al, 2008). The novelty of this work lies in the use of nonlinear dynamical-systems tools and recent results in LCS theory applied to collaborative robot tracking (Hsieh et al, 2012) to synthesize distributed control policies that enables AUVs to maintain a desired distribution in a fluidic environment.…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Distributed allocation of mobile sensing swarms in gyre flows

Mallory

Hsieh

Forgoston

et al. 2013

Nonlin. Processes Geophys.

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We address the synthesis of distributed control policies to enable a swarm of homogeneous mobile sensors to maintain a desired spatial distribution in a geophysical flow environment, or workspace. In this article, we assume the mobile sensors (or robots) have a "map" of the environment denoting the locations of the Lagrangian coherent structures or LCS boundaries. Using this information, we design agent-level hybrid control policies that leverage the surrounding fluid dynamics and inherent environmental noise to enable the team to maintain a desired distribution in the workspace. We discuss the stability properties of the ensemble dynamics of the distributed control policies. Since realistic quasi-geostrophic ocean models predict double-gyre flow solutions, we use a wind-driven multi-gyre flow model to verify the feasibility of the proposed distributed control strategy and compare the proposed control strategy with a baseline deterministic allocation strategy. Lastly, we validate the control strategy using actual flow data obtained by our coherent structure experimental testbed

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A Machine Learning Method for Improving Task Allocation in Distributed Multi-Robot Transportation

Cited by 18 publications

References 11 publications

Biologically inspired redistribution of a swarm of robots among multiple sites

Biologically inspired redistribution of a swarm of robots among multiple sites

Dynamic Partnership Formation for Multi-Rover Coordination

Distributed allocation of mobile sensing swarms in gyre flows

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