Multi-robot coalition formation

Vig, Lovekesh; Adams, Julie A.

doi:10.1109/tro.2006.878948

Cited by 242 publications

(152 citation statements)

References 67 publications

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“…In the research of multi-robot systems, the task allocation problem has also been studied extensively [5,6,12,16,20,22] (some of these are not necessarily restricted to the ST-MR-IA problem [5,16,22]). Some approaches are designed to achieve specific objectives or tasks [6,16,22].…”

Section: Related Workmentioning

confidence: 99%

Considering inter-task resource constraints in task allocation

Zhang

Parker

2012

Auton Agent Multi-Agent Syst

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This paper focuses on task allocation with single-task robots, multi-robot tasks and instantaneous assignment, which has been shown to be strongly NP-hard. Although this problem has been studied extensively, few efficient approximation algorithms have been provided due to its inherent complexity. In this paper, we first provide discussions and analyses for two natural greedy heuristics for solving this problem. Then, a new greedy heuristic is introduced, which considers inter-task resource constraints to approximate the influence between different assignments in task allocation. Instead of only looking at the utility of the assignment, our approach computes the expected loss of utility (due to the assigned robots and task) as an offset and uses the offset utility for making the greedy choice. A formal analysis is provided for the new heuristic, which reveals that the solution quality is bounded by two different factors. A new algorithm is then provided to approximate the new heuristic for performance improvement. Finally, for more complicated applications, we extend this problem to include general task dependencies and provide a result on the hardness of approximating this new formulation. Comparison results with the two natural heuristics in simulation are provided for both formulations, which show that the new approach achieves improved performance.

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Section: Related Workmentioning

confidence: 99%

Considering inter-task resource constraints in task allocation

Zhang

Parker

2012

Auton Agent Multi-Agent Syst

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“…ASyMTRe (Automated Synthesis of Multirobot Task solutions through software Reconfiguration) is the paradigm they use to generate multi-robot coalitions using complete information and it has been implemented on tasks that require multiple robots to share sensor and effector capabilities. Vig and Adams' approach (20) refers to a multi-robot coalition formation algorithm which uses an adaptation of Shehory and Kraus algorithm (17). The algorithm comprises two stages: in the first one robots compute the initial coalition values for all possible coalitions in a distributed way; in the second stage of the algorithm, robots agree on which coalitions should be formed.…”

Section: Related Workmentioning

confidence: 99%

“…In particular with respect to (20) we focus on a simpler setting, where tasks have a predefined priority and the value of a coalition can be computed by summing up the Self Evaluation 11 of the coalition members; in this way we can avoid the computation of all the possible coalition values needed for the algorithm presented in (17) and (20). Avoiding this computation in our setting is important because the value of a coalition is dependent on variables which are very dynamic (e.g., robot's heading, ball position etc. )…”

Section: Related Workmentioning

confidence: 99%

Cooperative control through objective achievement

Farinelli

Fujii²,

Tomoyasu³

et al. 2010

Robotics and Autonomous Systems

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Cooperative control is a key issue for multirobot systems in many practical applications. In this paper, we address the problem of coordinating a set of mobile robots in the RoboCup soccer middle size league. We show how the coordination problem that we face can be cast as a specific coalition formation problem and we propose a distributed algorithm to efficiently solve it. Our approach is based on the distributed computation of a measure of satisfaction (called Agent Satisfaction) that each agent computes for each task. We detail how, each agent computes the Agent Satisfaction by acquiring sensor perceptions through an omnidirectional vision system, extracting aggregated information from the acquired perception, and integrating such information with the ones communicated by the team mates. We empirically validate our approach in a simulated scenario and within the RoboCup competitions. The experiments in the simulated scenario allow us to analyse the behaviour of the algorithm in different situations, while the use of the algorithm in the real competitions validates the applicability of our approach to robotic platforms involved in a dynamic and complex scenario.

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“…(For example, see [1][2][3][4][5][6][7][8][9][10][11][12][13][14] for a recent, representative sample.) In fact, recent research suggests the use of graph-theoretic structures to represent formations, where vertices represent agents, and edges represent specific inter-agent distances to be maintained through decentralized control laws (see [15][16][17][18][19][20][21][22][23][24][25][26]).…”

Section: Introductionmentioning

confidence: 99%

Automatic Generation of Persistent Formations for Multi-Agent Networks Under Range Constraints

Smith¹,

Egerstedt²,

Howard³

2007

The Proceedings of First International Conference on Robot Communication and Coordination

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In this paper we present a collection of graphbased methods for determining if a team of mobile robots, subjected to sensor and communication range constraints, can persistently achieve a specified formation. What we mean by this is that the formation, once achieved, will be preserved by the direct maintenance of the smallest subset of all possible pairwise interagent distances. In this context, formations are defined by sets of points separated by distances corresponding to desired inter-agent distances. Further, we provide graph operations to describe agent interactions that implement a given formation, as well as an algorithm that, given a persistent formation, automatically generates a sequence of such operations. Experimental results are presented that illustrate the operation of the proposed methods on real robot platforms.

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Multi-robot coalition formation

Cited by 242 publications

References 67 publications

Considering inter-task resource constraints in task allocation

Considering inter-task resource constraints in task allocation

Cooperative control through objective achievement

Automatic Generation of Persistent Formations for Multi-Agent Networks Under Range Constraints

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