Auction-Based Task Allocation for Multi-robot Teams in Dynamic Environments

Schneider, Eric; Sklar, Elizabeth; Parsons, Simon; Ozgelen, A. Tuna

doi:10.1007/978-3-319-22416-9_29

Cited by 48 publications

(39 citation statements)

References 20 publications

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“…In some environments, a simple, greedy mechanism which might not be expected to perform well in the general case may, in fact, perform competitively with more sophisticated mechanisms, with the advantage of scaling better. Prior work has shown evidence that this is the case in both simulated and physical experiments [22][23][24].…”

Section: Introductionmentioning

confidence: 95%

“…Finally, a parameterised environment defines a task scenario, the starting locations of the team, and properties of tasks such as precedence ordering or arrival time. In the work presented here, all parameterised environments are, in the classification of [24], SR-IT-SA, that is, they comprise single-robot, independently ordered, statically allocated tasks.…”

Section: Definitions and System Architecturementioning

confidence: 99%

“…Our work makes use of MRTeAm, a software framework for conducting multirobot coordination experiments [23,24]. It is written on top of ROS [18] and its main components are a central auctioneer agent and multiple robot controller agents.…”

Section: Definitions and System Architecturementioning

confidence: 99%

“…In particular, in our earlier work, we showed [23,24] that while the sequential single-item auction [12] performs better than the parallel single-item auction [12] when the allocation is carried out with robots clustered together geographically, this advantage diminishes as robots are distributed over space and tasks are distributed over space and time. Based on this observation, in this paper we propose a portfolio-based approach to the MRTA problem.…”

Section: Introductionmentioning

confidence: 99%

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Mechanism Selection for Multi-Robot Task Allocation

Schneider

Sklar

Parsons

2017

Towards Autonomous Robotic Systems

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Abstract. The work presented here investigates how environmental features can be used to help select a task allocation mechanism from a portfolio in a multi-robot exploration scenario. In particular, we look at clusters of task locations and the positions of team members in relation to cluster centres. In a data-driven approach, we conduct experiments that use two different task allocation mechanisms on the same set of scenarios, providing comparative performance data. We then train a classifier on this data, giving us a method for choosing the best mechanism for a given scenario. We show that selecting a mechanism via this method, compared to using a single state-of-the-art mechanism only, can improve team performance in certain environments, according to our metrics.

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Section: Introductionmentioning

confidence: 95%

Section: Definitions and System Architecturementioning

confidence: 99%

Section: Definitions and System Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mechanism Selection for Multi-Robot Task Allocation

Schneider

Sklar

Parsons

2017

Towards Autonomous Robotic Systems

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“…Auctions are a form of market mechanism for resource allocation, and they can produce near optimal results in some MRR scenarios [15]. In MRR, auctions have been used to facilitate coordination amongst robots [26]; thus the same can be done with traffic signals. A common theme in the existing literature on auction-based traffic controllers is the need for a vehicle agent, which refers to a vehicle-borne software system responsible for tasks ranging from simple vehicle-to-infrastructure (V2I) communication to more demanding vehicle navigation and control.…”

Section: Introductionmentioning

confidence: 99%

An Intersection-Centric Auction-Based Traffic Signal Control Framework

Raphael¹,

Sklar²,

Maskell³

2017

Understanding Complex Systems

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Vehicular traffic on urban road networks is of great interest to those who monitor air quality. Combustion emissions from transport vehicles are a major contributor of air pollution. More specifically, the release of fine particulate matter which has been linked to premature deaths. Travel and idle time are two factors that influence the amount of pollution generated by traffic. Reducing idle and travel times would have a positive impact on air quality. Thus, it is increasingly crucial to manage intersections effectively, particularly in congested cities and across a range of different types of traffic conditions. A variety of market-based multi-agent traffic management mechanisms have been proposed to improve traffic flow. In many of these systems drivers "pay" to gain access to favourable road ways (e.g., minimise travel time). A major obstacle in adopting many of these mechanisms is that the necessary communication infrastructure does not yet exist. They rely on vehicle-toinfrastructure and/or vehicle-to-vehicle communications. In this work, we propose a market-based mechanism which relies on existing technology (and in some places this technology is already in use). Experimental results show that our market-based approach is better at reducing idle and travel times as compared to fixed-time signal controllers.

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