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Distributed Connectivity Control of Mobile Networks
, 2007
"... Control of mobile networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multiagent systems, a great new challenge is the development of distributed motion algorithms that guara ..."
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Cited by 75 (10 self)
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Control of mobile networks raises fundamental and novel problems in controlling the structure of the resulting dynamic graphs. In particular, in applications involving mobile sensor networks and multiagent systems, a great new challenge is the development of distributed motion algorithms that guarantee connectivity of the overall network. In this paper, we address this challenge using a novel control decomposition. First, motion control is performed in the continuous state space, where nearest neighbor potential fields are used to maintain existing links in the network. Second, distributed coordination protocols in the discrete graph space ensure connectivity of the switching network topology. Coordination is based on locally updated estimates of the abstract network topology by every agent as well as distributed auctions that enable tie breaking whenever simultaneous link deletions may violate connectivity. Integration of the overall system results in a distributed, multiagent, hybrid system for which we show that, under certain secondary objectives on the agents and the assumption that the initial network is connected, the resulting motion always satisfies connectivity of the network. Our approach can also account for communication time delays in the network as well as collision avoidance, while its efficiency is illustrated in nontrivial computer simulations.
Distributed control of robotic networks: a mathematical approach to motion coordination algorithms
, 2009
"... (i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 ..."
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Cited by 41 (1 self)
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(i) You are allowed to freely download, share, print, or photocopy this document. (ii) You are not allowed to modify, sell, or claim authorship of any part of this document. (iii) We thank you for any feedback information, including errors, suggestions, evaluations, and teaching or research uses. 2 “Distributed Control of Robotic Networks ” by F. Bullo, J. Cortés and S. Martínez
A Distributed Auction Algorithm for the Assignment Problem
, 2008
"... The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problems’ size and the cost for global computation and inform ..."
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Cited by 29 (0 self)
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The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problems’ size and the cost for global computation and information can be highly prohibitive, gives rise to the need for local solutions that dynamically assign distinct agents to distinct tasks, while maximizing the total assignment benefit. In this paper, we consider the linear assignment problem in the context of networked systems, where the main challenge is dealing with the lack of global information due to the limited communication capabilities of the agents. We address this challenge by means of a distributed auction algorithm, where the agents are able to bid for the task to which they wish to be assigned. The desired assignment relies on an appropriate selection of bids that determine the prices of the tasks and render them more or less attractive for the agents to bid for. Up to date pricing information, necessary for accurate bidding, can be obtained in a multihop fashion by means of local communication between adjacent agents. Our algorithm is an extension to the parallel auction algorithm proposed by Bertsekas et al to the case where only local information is available and it is shown to always converge to an assignment that maximizes the total assignment benefit within a linear approximation of the optimal one.
A dynamical systems approach to weighted graph matching.
 Proceedings of the 45th IEEE conference on decision and control,
, 2006
"... Abstract Graph matching is a fundamental problem that arises frequently in the areas of distributed control, computer vision, and facility allocation. In this paper, we consider the optimal graph matching problem for weighted graphs, which is computationally challenging due the combinatorial nature ..."
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Cited by 14 (4 self)
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Abstract Graph matching is a fundamental problem that arises frequently in the areas of distributed control, computer vision, and facility allocation. In this paper, we consider the optimal graph matching problem for weighted graphs, which is computationally challenging due the combinatorial nature of the set of permutations. Contrary to optimizationbased relaxations to this problem, in this paper we develop a novel relaxation by constructing dynamical systems on the manifold of orthogonal matrices. In particular, since permutation matrices are orthogonal matrices with nonnegative elements, we define two gradient flows in the space of orthogonal matrices. The first minimizes the cost of weighted graph matching over orthogonal matrices, whereas the second minimizes the distance of an orthogonal matrix from the finite set of all permutations. The combination of the two dynamical systems converges to a permutation matrix, which provides a suboptimal solution to the weighted graph matching problem. Finally, our approach is shown to be promising by illustrating it on nontrivial problems.
MultiLevel Partitioning and Distribution of the Assignment Problem for LargeScale MultiRobot Task Allocation
"... Abstract — A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentral ..."
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Cited by 8 (1 self)
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Abstract — A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentralized approaches at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. We depart from optimization and bipartite matching formulations of the problem, observing instead that an assignment can be computed through coarsening and partitioning operations on the utility matrix. First, a coarse assignment is calculated by evaluating the global utility information and partitioning it into clusters in a problemdomain independent way. Next, the assignment solutions in each partition are refined (either recursively, or via an existing algorithm). This multilevel framework allows the repeated reassignment to execute among interrelated partitions. The results suggest that only a minor sacrifice in solution quality is required for gains in efficiency. The proposed algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms. I.
Largescale multirobot task allocation via dynamic partitioning and distribution. Autonomous Robots 33(3):291–307
, 2012
"... This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and ..."
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Cited by 7 (3 self)
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This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and partitioning operations on the standard utility matrix via a set of mature partitioning techniques and programs. The algorithm mixes centralized and decentralized approaches dynamically at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. An allocation results by operating on each partition: either the steps are repeated recursively to refine the generalized assignment, or each subproblem may be solved by an existing algorithm. The results suggest that only a minor sacrifice in solution quality is needed for significant gains in efficiency. The algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms.
Decentralized Task Allocation for Dynamic Environments
, 2012
"... This thesis presents an overview of the design process for creating greedy decentralized task allocation algorithms and outlines the main decisions that progressed the algorithm through three different forms. The first form was called the Sequential Greedy Algorithm (SGA). This algorithm, although ..."
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Cited by 6 (2 self)
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This thesis presents an overview of the design process for creating greedy decentralized task allocation algorithms and outlines the main decisions that progressed the algorithm through three different forms. The first form was called the Sequential Greedy Algorithm (SGA). This algorithm, although fast, relied on a large number of iterations to converge, which slowed convergence in decentralized environments. The second form was called the Consensus Based Bundle Algorithm (CBBA). CBBA required significantly fewer iterations than SGA but it is noted that both still rely on global synchronization mechanisms. These synchronization mechanisms end up being difficult to enforce in decentralized environments. The main result of this thesis is the creation of the Asynchronous Consensus Based Bundle Algorithm (ACBBA). ACBBA broke the global synchronous assumptions of CBBA and SGA to allow each agent more autonomy and thus provided more robustness to the task allocation so
Stability analysis for the NullSpacebased Behavioral control for multirobot systems
 In 47th IEEE Conference on Decision and Control and 8th European Control Conference, Cancun, MEX
, 2008
"... Abstract — A wide number of mobile multirobot systems makes use of behaviorbased approaches to accomplish their missions. However, despite the advantages in term of flexibility and versatility, the behaviorbased approaches often lack of a rigorous stability analysis; when the latter is present it ..."
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Cited by 5 (4 self)
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Abstract — A wide number of mobile multirobot systems makes use of behaviorbased approaches to accomplish their missions. However, despite the advantages in term of flexibility and versatility, the behaviorbased approaches often lack of a rigorous stability analysis; when the latter is present it is only valid for specific missions. A behavior basedapproach for the control of different robotic systems, namely the NullSpacebased Behavioral control, has been recently proposed. In this paper, its general stability analysis is discussed following a Lyapunovbased approach; moreover, effective conditions are given to verify that the behaviors of specific missions are properly defined and merged. Finally, the stability of several missions for multirobot systems is discussed. I.
Distributed algorithms for robotic networks
 In R. Meyers (Ed.), Encyclopedia of Complexity and Systems Science
, 2008
"... ..."
Consensusbased Robust Decentralized Task Assignment for Heterogeneous Robot Networks
"... Abstract — This paper considers the problem of decentralized task assignment in a network of heterogeneous robots. We introduce a new algorithm named heterogeneous robots consensusbased allocation (HRCA), which can be viewed as a possible extension of the recently proposed consensusbased bundle a ..."
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Cited by 3 (1 self)
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Abstract — This paper considers the problem of decentralized task assignment in a network of heterogeneous robots. We introduce a new algorithm named heterogeneous robots consensusbased allocation (HRCA), which can be viewed as a possible extension of the recently proposed consensusbased bundle algorithm (CBBA) for homogeneous robot networks. The HRCA is based on a two stage decentralized procedure. In the first stage, similarly to CBBA, an initial assignment based on marketbased decision strategies and local communication is determined, disregarding possible constraints on the maximum number of tasks assignable to each robot. Constraint violations are handled in the second stage, in which an iterative procedure is used by the robots to redistribute the tasks exceeding their individual capacity with minimal losses in terms of score function. Numerical simulations are used to evaluate the performance of the HRCA in a set of randomly generated scenarios, which include some examples of homogeneous networks to allow a comparison with CBBA. I.