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— 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(More)
We consider the problem of multi-robot task-allocation when robots have to deal with uncertain utility estimates. Typically an allocation is performed to maximize expected utility; we consider a means for measuring the robustness of a given optimal allocation when robots have some measure of the uncertainty (e.g., a probability distribution, or moments of(More)
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(More)
— The assignment problem arises in multi-robot task-allocation scenarios. This paper introduces an algorithm for solving the assignment problem with several appealing features for online, distributed robotics applications. The method can start with any initial matching and incrementally improve the solution to reach the global optimum, producing valid(More)
— In scenarios in which new robots and tasks are added to a network of already deployed, interchangeable robots, a trade-off arises in minimizing the cost to execute the tasks and the level of disruption to the system. This paper considers a navigation-oriented variant of this problem and proposes a parametrizable method to adjust the optimization(More)
The assignment problem arises in multi-robot task-allocation scenarios. Inspired by existing techniques that employ task exchanges between robots, this paper introduces an algorithm for solving the assignment problem that has several appealing features for online, distributed robotics applications. The method may start with any initial matching and(More)
— Many multi-robot scenarios involve navigation of a set of networked robots through a constrained environment to achieve coverage, maintain a predefined shape, sense at prede-fined locations, or to satisfy some other distance-defined property. When new robots and tasks are added to a network of already deployed interchangeable robots, a trade-off arises in(More)
Pfam ID Pfam HMM name Known activities GH CW TM Glycoside hydrolase catalytic domains pfam00232 GH_1 β-Glucosidase, β-galactosidase, β-mannosidase, others 34 181 27 pfam00703 GH_2 β-galactosidase, β-mannosidase, others 1 9 33 pfam02836 GH_2_C β-galactosidase, β-mannosidase, others 1 13 40 pfam02837 GH_2_N Sugar-binding domain 1 10 48 pfam00933 GH_3(More)
—Auction and market-based mechanisms are among the most popular methods for distributed task allocation in multi-robot systems. Most of these mechanisms were designed in a heuristic way and analysis of the quality of the resulting assignment solution is rare. This paper presents a new market-based multi-robot task allocation algorithm that produces optimal(More)